commit 070abb6c7a4f6f1d158e26abbdff487644a52959
Author: kassam <kassam@yslootahtech.com>
Date:   Wed Aug 5 14:11:32 2026 +0400

    Update 2026-08-05 14:11:31

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c02264b
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+__pycache__/
+*.pyc
+Logs/
+*.log
diff --git a/Audio_Recorder/DataG1/greeting.wav b/Audio_Recorder/DataG1/greeting.wav
new file mode 100644
index 0000000..4d74e4b
Binary files /dev/null and b/Audio_Recorder/DataG1/greeting.wav differ
diff --git a/Audio_Recorder/g1_audio_devices.py b/Audio_Recorder/g1_audio_devices.py
new file mode 100644
index 0000000..00a23fb
--- /dev/null
+++ b/Audio_Recorder/g1_audio_devices.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""
+G1 AUDIO DEVICES — PulseAudio source/sink enumeration + Hollyland auto-setup.
+
+Pure-Python wrapper around `pactl`. No PulseAudio Python bindings required.
+Safe to run on workstation or on the robot.
+
+Capabilities:
+  list              — list every PulseAudio source and sink
+  find <keyword>    — find a source/sink by substring (case-insensitive)
+  auto-hollyland    — detect Hollyland wireless mic, unmute, set default, volume 100%
+  quick-test <idx>  — record 2 s from source <idx> via parec and report stats
+  mute <idx>        — set-source-mute 1
+  unmute <idx>      — set-source-mute 0
+  volume <idx> <%>  — set-source-volume <pct>%
+
+See voice_note.txt for why parec is preferred over PyAudio on the robot.
+
+Usage:
+  python3 g1_audio_devices.py list
+  python3 g1_audio_devices.py find hollyland
+  python3 g1_audio_devices.py auto-hollyland
+  python3 g1_audio_devices.py quick-test 3
+"""
+
+from __future__ import annotations
+
+import argparse
+import subprocess
+import sys
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable
+
+
+HOLLYLAND_KEYWORDS = ("hollyland", "wireless_microphone", "shenzhen_hollyland")
+ANKER_KEYWORDS = ("anker", "powerconf")
+
+
+@dataclass
+class PulseDevice:
+    index: int
+    name: str
+    description: str
+    state: str
+    mute: bool | None = None
+    volume_pct: int | None = None
+
+
+def _run(cmd: list[str]) -> tuple[int, str, str]:
+    try:
+        p = subprocess.run(cmd, capture_output=True, text=True)
+    except FileNotFoundError:
+        tool = cmd[0]
+        print(
+            f"error: `{tool}` not on PATH. Install PulseAudio tools "
+            f"(e.g. `sudo apt install pulseaudio-utils`) or run this on the robot.",
+            file=sys.stderr,
+        )
+        sys.exit(127)
+    return p.returncode, p.stdout, p.stderr
+
+
+def _list(kind: str) -> list[PulseDevice]:
+    """kind = 'sources' or 'sinks'."""
+    rc, out, _ = _run(["pactl", "list", "short", kind])
+    devices: list[PulseDevice] = []
+    if rc != 0:
+        return devices
+    for line in out.splitlines():
+        parts = line.split("\t")
+        if len(parts) < 4:
+            continue
+        idx, name, _driver, _format = parts[0], parts[1], parts[2], parts[3]
+        state = parts[4] if len(parts) > 4 else ""
+        devices.append(PulseDevice(int(idx), name, "", state))
+    # Enrich with mute + volume from `pactl list {sources,sinks}` (long form).
+    rc, long_out, _ = _run(["pactl", "list", kind])
+    if rc != 0:
+        return devices
+    blocks = long_out.split("\n\n")
+    for block in blocks:
+        lines = block.strip().splitlines()
+        if not lines:
+            continue
+        try:
+            header_idx = int(lines[0].split("#")[-1].strip())
+        except ValueError:
+            continue
+        mute = None
+        volume = None
+        desc = ""
+        for ln in lines:
+            s = ln.strip()
+            if s.startswith("Mute:"):
+                mute = s.endswith("yes")
+            elif s.startswith("Volume:"):
+                # "Volume: front-left: 65536 / 100% / 0.00 dB, ..."
+                pct_token = None
+                for tok in s.split():
+                    if tok.endswith("%") and tok[:-1].lstrip("-").isdigit():
+                        pct_token = tok
+                        break
+                if pct_token:
+                    try:
+                        volume = int(pct_token.rstrip("%"))
+                    except ValueError:
+                        pass
+            elif s.startswith("Description:"):
+                desc = s.split(":", 1)[1].strip()
+        for d in devices:
+            if d.index == header_idx:
+                d.mute = mute
+                d.volume_pct = volume
+                if desc and not d.description:
+                    d.description = desc
+                break
+    return devices
+
+
+def list_sources() -> list[PulseDevice]:
+    return _list("sources")
+
+
+def list_sinks() -> list[PulseDevice]:
+    return _list("sinks")
+
+
+def find_by_keywords(
+    devices: Iterable[PulseDevice], keywords: tuple[str, ...]
+) -> PulseDevice | None:
+    for d in devices:
+        haystack = f"{d.name} {d.description}".lower()
+        if any(k in haystack for k in keywords):
+            return d
+    return None
+
+
+def pprint_devices(title: str, devices: Iterable[PulseDevice]) -> None:
+    print(f"\n{title}")
+    print("-" * len(title))
+    if not devices:
+        print("  (none)")
+        return
+    for d in devices:
+        mute = "muted" if d.mute else "ok"
+        vol = f"{d.volume_pct}%" if d.volume_pct is not None else "?"
+        print(f"  [{d.index:>2}] {d.name}")
+        if d.description:
+            print(f"       desc: {d.description}")
+        print(f"       state={d.state}  {mute}  vol={vol}")
+
+
+def set_mute(index: int, mute: bool) -> None:
+    _run(["pactl", "set-source-mute", str(index), "1" if mute else "0"])
+
+
+def set_volume(index: int, pct: int) -> None:
+    _run(["pactl", "set-source-volume", str(index), f"{pct}%"])
+
+
+def set_default_source(name: str) -> None:
+    _run(["pactl", "set-default-source", name])
+
+
+def auto_hollyland() -> int:
+    """Follow the voice_note.txt recipe to bring the Hollyland mic online."""
+    sources = list_sources()
+    mic = find_by_keywords(sources, HOLLYLAND_KEYWORDS)
+    if mic is None:
+        print("Hollyland mic not found. Plug it in and retry.")
+        pprint_devices("Current sources", sources)
+        return 1
+    print(f"Found: [{mic.index}] {mic.name}")
+    set_default_source(mic.name)
+    set_mute(mic.index, False)
+    set_volume(mic.index, 100)
+    print(f"Set as default source, unmuted, volume 100%.")
+    return quick_test_capture(mic.index, seconds=2)
+
+
+def quick_test_capture(index: int, seconds: float = 2.0) -> int:
+    """Record a few seconds with parec and report sample statistics."""
+    import numpy as np
+
+    with tempfile.NamedTemporaryFile(suffix=".pcm", delete=False) as tmp:
+        tmp_path = Path(tmp.name)
+    cmd = [
+        "parec", "-d", str(index),
+        "--format=s16le", "--rate=16000", "--channels=1", "--raw",
+    ]
+    print(f"Capturing {seconds:.1f}s from source {index} ...")
+    proc = subprocess.Popen(cmd, stdout=open(tmp_path, "wb"))
+    try:
+        proc.wait(timeout=seconds + 2)
+    except subprocess.TimeoutExpired:
+        proc.terminate()
+    finally:
+        proc.poll()
+    try:
+        data = np.fromfile(tmp_path, dtype=np.int16)
+    finally:
+        tmp_path.unlink(missing_ok=True)
+
+    if data.size == 0:
+        print("  No samples captured. Check device index and that parec is installed.")
+        return 2
+    rms = float((data.astype("float32") ** 2).mean()) ** 0.5
+    peak = int(abs(data).max())
+    print(f"  samples={data.size}  peak={peak}  rms={rms:.0f}")
+    if rms > 50:
+        print("  MIC WORKS.")
+        return 0
+    print("  Signal looks silent — check transmitter is on + mic not muted + volume up.")
+    return 3
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="G1 audio device helper (pactl wrapper)")
+    sub = p.add_subparsers(dest="cmd", required=True)
+    sub.add_parser("list", help="List sources and sinks")
+    f = sub.add_parser("find", help="Find a device by substring")
+    f.add_argument("keyword")
+    sub.add_parser("auto-hollyland", help="Auto-detect Hollyland mic and set up")
+    q = sub.add_parser("quick-test", help="Record 2s from a source and report")
+    q.add_argument("index", type=int)
+    q.add_argument("--seconds", type=float, default=2.0)
+    m = sub.add_parser("mute", help="Mute a source")
+    m.add_argument("index", type=int)
+    u = sub.add_parser("unmute", help="Unmute a source")
+    u.add_argument("index", type=int)
+    v = sub.add_parser("volume", help="Set source volume percent")
+    v.add_argument("index", type=int)
+    v.add_argument("percent", type=int)
+    args = p.parse_args()
+
+    if args.cmd == "list":
+        pprint_devices("SOURCES (mics)", list_sources())
+        pprint_devices("SINKS (speakers)", list_sinks())
+        return 0
+    if args.cmd == "find":
+        kw = args.keyword.lower()
+        src = find_by_keywords(list_sources(), (kw,))
+        snk = find_by_keywords(list_sinks(), (kw,))
+        if src:
+            print(f"source [{src.index}] {src.name}")
+        if snk:
+            print(f"sink   [{snk.index}] {snk.name}")
+        if not src and not snk:
+            print(f"No device matches '{args.keyword}'")
+            return 1
+        return 0
+    if args.cmd == "auto-hollyland":
+        return auto_hollyland()
+    if args.cmd == "quick-test":
+        return quick_test_capture(args.index, args.seconds)
+    if args.cmd == "mute":
+        set_mute(args.index, True)
+        return 0
+    if args.cmd == "unmute":
+        set_mute(args.index, False)
+        return 0
+    if args.cmd == "volume":
+        set_volume(args.index, args.percent)
+        return 0
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Audio_Recorder/g1_edge_tts.py b/Audio_Recorder/g1_edge_tts.py
new file mode 100644
index 0000000..4f276d9
--- /dev/null
+++ b/Audio_Recorder/g1_edge_tts.py
@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+"""
+G1 EDGE-TTS — Text to G1 speaker via Microsoft Edge TTS (no API key).
+
+Pipeline matches Marcus's working path documented in voice_note.txt:
+    edge-tts  →  MP3 bytes  →  pydub 16 kHz mono WAV
+              →  _CallRequestWithParamAndBin on G1 AudioClient
+
+Voices (defaults):
+    Arabic   ar-AE-HamdanNeural   (UAE male)
+    English  en-US-GuyNeural      (US male)
+
+Runs on robot (has AudioClient) OR on workstation (save WAV only with --save).
+
+Requirements:
+    pip install edge-tts pydub numpy
+    (on robot use the gemini conda env which has the audio SDK methods)
+
+Usage:
+    # Speak directly on G1 speaker:
+    python3 g1_edge_tts.py "مرحبا بكم" --lang ar
+    python3 g1_edge_tts.py "Hello welcome to Lootah" --lang en
+
+    # Just save a WAV (no robot needed):
+    python3 g1_edge_tts.py "Good morning" --save greeting.wav
+
+    # Override voice:
+    python3 g1_edge_tts.py "test" --lang ar --voice ar-SA-HamedNeural
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import io
+import json
+import sys
+import time
+import wave
+from pathlib import Path
+
+import numpy as np
+
+DEFAULT_VOICES = {
+    "ar": "ar-AE-HamdanNeural",
+    "en": "en-US-GuyNeural",
+}
+TARGET_RATE = 16000
+APP_NAME = "edge_tts"
+
+
+async def _synthesize(text: str, voice: str) -> bytes:
+    """Call edge-tts and return raw MP3 bytes."""
+    import edge_tts  # lazy import — only needed when actually speaking
+
+    communicate = edge_tts.Communicate(text=text, voice=voice)
+    buf = io.BytesIO()
+    async for chunk in communicate.stream():
+        if chunk["type"] == "audio":
+            buf.write(chunk["data"])
+    return buf.getvalue()
+
+
+def _mp3_to_pcm16k(mp3: bytes) -> np.ndarray:
+    """Decode MP3 → 16 kHz mono int16."""
+    from pydub import AudioSegment  # lazy — pulls ffmpeg at import time
+
+    seg = AudioSegment.from_file(io.BytesIO(mp3), format="mp3")
+    seg = seg.set_channels(1).set_frame_rate(TARGET_RATE).set_sample_width(2)
+    return np.frombuffer(seg.raw_data, dtype=np.int16)
+
+
+def save_wav(pcm: np.ndarray, path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with wave.open(str(path), "wb") as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(2)
+        wf.setframerate(TARGET_RATE)
+        wf.writeframes(pcm.tobytes())
+
+
+def _play_on_g1(pcm: np.ndarray, interface: str = "eth0", volume: int = 100) -> float:
+    """Single-call _CallRequestWithParamAndBin — the working method."""
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+    from unitree_sdk2py.g1.audio.g1_audio_api import (
+        ROBOT_API_ID_AUDIO_START_PLAY,
+        ROBOT_API_ID_AUDIO_STOP_PLAY,
+    )
+
+    ChannelFactoryInitialize(0, interface)
+    c = AudioClient()
+    c.SetTimeout(10.0)
+    c.Init()
+    c.SetVolume(volume)
+
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    time.sleep(0.3)
+
+    pcm_bytes = pcm.tobytes()
+    duration = len(pcm) / TARGET_RATE
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": APP_NAME,
+        "stream_id": sid,
+        "sample_rate": TARGET_RATE,
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+    start = time.time()
+    c._CallRequestWithParamAndBin(
+        ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm_bytes)
+    )
+    time.sleep(duration + 0.5)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    return time.time() - start
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="Edge-TTS → G1 speaker (AR + EN)")
+    p.add_argument("text", help="Text to speak (wrap in quotes)")
+    p.add_argument("--lang", choices=["ar", "en"], default="en",
+                   help="Language (default: en)")
+    p.add_argument("--voice", default=None,
+                   help="Override voice (e.g. ar-SA-HamedNeural)")
+    p.add_argument("--save", metavar="PATH",
+                   help="Also save the synthesized 16 kHz WAV to PATH")
+    p.add_argument("--no-play", action="store_true",
+                   help="Skip G1 playback; useful with --save from workstation")
+    p.add_argument("--interface", default="eth0", help="DDS interface (default: eth0)")
+    p.add_argument("--volume", type=int, default=100, help="Speaker volume 0-100")
+    args = p.parse_args()
+
+    voice = args.voice or DEFAULT_VOICES[args.lang]
+    print(f"Synthesizing ({args.lang}, voice={voice}) ...")
+    t0 = time.time()
+    try:
+        mp3 = asyncio.run(_synthesize(args.text, voice))
+    except Exception as exc:
+        print(f"edge-tts failed: {exc}", file=sys.stderr)
+        return 1
+    pcm = _mp3_to_pcm16k(mp3)
+    dur = len(pcm) / TARGET_RATE
+    print(f"  {len(mp3):,} MP3 bytes -> {dur:.1f}s WAV ({time.time() - t0:.1f}s synth)")
+
+    if args.save:
+        save_path = Path(args.save)
+        save_wav(pcm, save_path)
+        print(f"  saved {save_path} ({save_path.stat().st_size/1024:.1f} KB)")
+
+    if args.no_play:
+        return 0
+
+    try:
+        elapsed = _play_on_g1(pcm, args.interface, args.volume)
+        print(f"Played on G1 ({elapsed:.1f}s).")
+    except Exception as exc:
+        print(f"G1 playback failed: {exc}", file=sys.stderr)
+        print("Hint: run this on the robot inside the gemini conda env.")
+        return 2
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Audio_Recorder/g1_interactive_player.py b/Audio_Recorder/g1_interactive_player.py
new file mode 100644
index 0000000..115cc47
--- /dev/null
+++ b/Audio_Recorder/g1_interactive_player.py
@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""
+G1 Interactive Voice Player
+----------------------------
+Lists WAV files and lets you pick which ones to play on the G1 built-in speaker.
+Uses _CallRequestWithParamAndBin with format params (the working method).
+
+Usage (run ON the robot):
+  python3 g1_interactive_player.py "/home/unitree/SanadVoice/recorded voices"
+  python3 g1_interactive_player.py "/home/unitree/SanadVoice/recorded voices" --volume 80
+"""
+
+import sys
+import time
+import wave
+import json
+import argparse
+import numpy as np
+from pathlib import Path
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+from unitree_sdk2py.g1.audio.g1_audio_api import (
+    ROBOT_API_ID_AUDIO_START_PLAY,
+    ROBOT_API_ID_AUDIO_STOP_PLAY,
+)
+
+TARGET_RATE = 16000
+APP_NAME = "p"
+
+
+def load_wav(path: str):
+    with wave.open(path, "rb") as wf:
+        rate = wf.getframerate()
+        nch = wf.getnchannels()
+        audio = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
+
+    if nch == 2:
+        audio = audio.reshape(-1, 2).mean(axis=1).astype(np.int16)
+
+    if rate != TARGET_RATE:
+        tl = int(len(audio) * TARGET_RATE / rate)
+        audio = np.interp(
+            np.linspace(0, len(audio), tl, endpoint=False),
+            np.arange(len(audio)),
+            audio.astype(np.float64),
+        ).astype(np.int16)
+
+    return audio
+
+
+def play_pcm(client, pcm: np.ndarray):
+    # Single call with a unique stream_id — chunked repeats with the same sid
+    # are dropped by the audio service, per voice_note.txt.
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    time.sleep(0.2)
+
+    pcm_bytes = pcm.tobytes()
+    duration = len(pcm) / TARGET_RATE
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": APP_NAME,
+        "stream_id": sid,
+        "sample_rate": TARGET_RATE,
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+
+    start = time.time()
+    client._CallRequestWithParamAndBin(
+        ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm_bytes)
+    )
+    time.sleep(duration + 0.3)
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    return time.time() - start
+
+
+def show_menu(wavs):
+    print(f"\n{'#':<4} {'Name':<45} {'Duration':>8}")
+    print("-" * 59)
+    for i, (p, dur) in enumerate(wavs):
+        print(f"{i:<4} {p.stem:<45} {dur:>6.1f}s")
+    print("-" * 59)
+    print(f"{'a':<4} {'Play ALL':<45}")
+    print(f"{'m':<4} {'Show menu':<45}")
+    print(f"{'q':<4} {'Quit':<45}")
+    print()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="G1 Interactive Voice Player")
+    parser.add_argument("directory", help="Directory containing WAV files")
+    parser.add_argument("--volume", "-v", type=int, default=100, help="Volume 0-100")
+    parser.add_argument("--interface", "-i", default="eth0", help="Network interface")
+
+    args = parser.parse_args()
+
+    wav_dir = Path(args.directory)
+    if not wav_dir.is_dir():
+        print(f"Not a directory: {wav_dir}")
+        sys.exit(1)
+
+    wav_files = []
+    for p in sorted(wav_dir.glob("*.wav")):
+        try:
+            with wave.open(str(p), "rb") as wf:
+                dur = wf.getnframes() / wf.getframerate()
+            wav_files.append((p, dur))
+        except Exception:
+            pass
+
+    if not wav_files:
+        print(f"No WAV files found in {wav_dir}")
+        sys.exit(1)
+
+    print(f"Connecting to G1 speaker...")
+    ChannelFactoryInitialize(0, args.interface)
+    client = AudioClient()
+    client.SetTimeout(10.0)
+    client.Init()
+    client.SetVolume(args.volume)
+    print(f"Ready. Volume: {args.volume}%")
+
+    show_menu(wav_files)
+
+    while True:
+        try:
+            choice = input("Pick a number (or a/m/q): ").strip().lower()
+        except (KeyboardInterrupt, EOFError):
+            print("\nBye.")
+            break
+
+        if choice == "q":
+            print("Bye.")
+            break
+
+        if choice == "m":
+            show_menu(wav_files)
+            continue
+
+        if choice == "a":
+            print(f"\nPlaying all {len(wav_files)} files...\n")
+            for i, (p, dur) in enumerate(wav_files):
+                print(f"  [{i+1}/{len(wav_files)}] {p.stem} ({dur:.1f}s)...", end=" ", flush=True)
+                pcm = load_wav(str(p))
+                elapsed = play_pcm(client, pcm)
+                print(f"done ({elapsed:.1f}s)")
+                time.sleep(0.5)
+            print("\nAll done.\n")
+            continue
+
+        indices = []
+        for part in choice.replace(" ", "").split(","):
+            if "-" in part:
+                try:
+                    a, b = part.split("-", 1)
+                    indices.extend(range(int(a), int(b) + 1))
+                except ValueError:
+                    pass
+            else:
+                try:
+                    indices.append(int(part))
+                except ValueError:
+                    pass
+
+        if not indices:
+            print("Invalid. Enter number, range (2-5), list (1,3,5), a, m, or q.")
+            continue
+
+        for idx in indices:
+            if idx < 0 or idx >= len(wav_files):
+                print(f"  #{idx} out of range, skipping.")
+                continue
+
+            p, dur = wav_files[idx]
+            print(f"  Playing: {p.stem} ({dur:.1f}s)...", end=" ", flush=True)
+            pcm = load_wav(str(p))
+            elapsed = play_pcm(client, pcm)
+            print(f"done ({elapsed:.1f}s)")
+
+        print()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/g1_play_wav.py b/Audio_Recorder/g1_play_wav.py
new file mode 100644
index 0000000..fd502ca
--- /dev/null
+++ b/Audio_Recorder/g1_play_wav.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+"""
+G1 WAV Player — plays WAV files through the G1 built-in speaker.
+Runs ON the robot via AudioClient._CallRequestWithParamAndBin.
+
+(PlayStream chunked mode is unreliable — see voice_note.txt.)
+
+Usage:
+  python3 g1_play_wav.py <wav_file>
+  python3 g1_play_wav.py <wav_file> --volume 80
+  python3 g1_play_wav.py --list <directory>
+  python3 g1_play_wav.py --all <directory>
+
+Examples:
+  python3 g1_play_wav.py "/home/unitree/SanadVoice/recorded voices/welcome_single.wav"
+  python3 g1_play_wav.py --list "/home/unitree/SanadVoice/recorded voices"
+  python3 g1_play_wav.py --all "/home/unitree/SanadVoice/recorded voices"
+"""
+
+import sys
+import time
+import wave
+import json
+import argparse
+import numpy as np
+from pathlib import Path
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+from unitree_sdk2py.g1.audio.g1_audio_api import (
+    ROBOT_API_ID_AUDIO_START_PLAY,
+    ROBOT_API_ID_AUDIO_STOP_PLAY,
+)
+
+TARGET_RATE = 16000
+APP_NAME = "g1_player"
+
+
+def init_client(interface: str = "eth0", volume: int = 100):
+    ChannelFactoryInitialize(0, interface)
+    client = AudioClient()
+    client.SetTimeout(10.0)
+    client.Init()
+    client.SetVolume(volume)
+    return client
+
+
+def load_wav(path: str):
+    """Load WAV and convert to 16kHz mono 16-bit PCM."""
+    with wave.open(path, "rb") as wf:
+        rate = wf.getframerate()
+        nch = wf.getnchannels()
+        audio = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
+
+    if nch == 2:
+        audio = audio.reshape(-1, 2).mean(axis=1).astype(np.int16)
+
+    if rate != TARGET_RATE:
+        target_len = int(len(audio) * TARGET_RATE / rate)
+        audio = np.interp(
+            np.linspace(0, len(audio), target_len, endpoint=False),
+            np.arange(len(audio)),
+            audio.astype(np.float64),
+        ).astype(np.int16)
+
+    return audio
+
+
+def play_wav(client, pcm: np.ndarray, label: str = ""):
+    """Send PCM to G1 speaker in ONE call (the working method)."""
+    pcm_bytes = pcm.tobytes()
+    duration = len(pcm) / TARGET_RATE
+
+    if label:
+        print(f"  {label} ({duration:.1f}s)...", end=" ", flush=True)
+    else:
+        print(f"  Playing {duration:.1f}s...", end=" ", flush=True)
+
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    time.sleep(0.2)
+
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": APP_NAME,
+        "stream_id": sid,
+        "sample_rate": TARGET_RATE,
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+
+    start = time.time()
+    client._CallRequestWithParamAndBin(
+        ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm_bytes)
+    )
+    time.sleep(duration + 0.5)
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    print(f"done ({time.time() - start:.1f}s)")
+
+
+def list_wavs(directory: str):
+    """List all WAV files in a directory."""
+    wavs = sorted(Path(directory).glob("*.wav"))
+    if not wavs:
+        print(f"No WAV files in {directory}")
+        return []
+
+    print(f"\n{'#':<4} {'Name':<40} {'Duration':>10}")
+    print("-" * 56)
+    for i, p in enumerate(wavs):
+        try:
+            with wave.open(str(p), "rb") as wf:
+                dur = wf.getnframes() / wf.getframerate()
+            print(f"{i:<4} {p.stem:<40} {dur:>8.1f}s")
+        except Exception as e:
+            print(f"{i:<4} {p.stem:<40} {'ERROR':>10}")
+    print()
+    return wavs
+
+
+def main():
+    parser = argparse.ArgumentParser(description="G1 WAV Player (runs on robot)")
+    parser.add_argument("path", nargs="?", help="WAV file or directory path")
+    parser.add_argument("--list", metavar="DIR", help="List WAV files in directory")
+    parser.add_argument("--all", metavar="DIR", help="Play all WAV files in directory")
+    parser.add_argument("--volume", "-v", type=int, default=100, help="Volume 0-100")
+    parser.add_argument("--interface", "-i", default="eth0", help="Network interface")
+    parser.add_argument("--pause", "-p", type=float, default=1.0,
+                        help="Pause between files in --all mode (seconds)")
+
+    args = parser.parse_args()
+
+    if args.list:
+        list_wavs(args.list)
+        return
+
+    if args.all:
+        wavs = sorted(Path(args.all).glob("*.wav"))
+        if not wavs:
+            print(f"No WAV files in {args.all}")
+            return
+
+        client = init_client(args.interface, args.volume)
+        print(f"\nPlaying {len(wavs)} files from {args.all}\n")
+
+        for i, wav_path in enumerate(wavs):
+            pcm = load_wav(str(wav_path))
+            play_wav(client, pcm, label=f"[{i+1}/{len(wavs)}] {wav_path.stem}")
+            if i < len(wavs) - 1:
+                time.sleep(args.pause)
+
+        print(f"\nAll done.")
+        return
+
+    if not args.path:
+        parser.print_help()
+        return
+
+    path = Path(args.path)
+    if not path.exists():
+        print(f"File not found: {path}")
+        sys.exit(1)
+
+    client = init_client(args.interface, args.volume)
+    pcm = load_wav(str(path))
+    play_wav(client, pcm, label=path.stem)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/g1_tts_arabic.py b/Audio_Recorder/g1_tts_arabic.py
new file mode 100644
index 0000000..b92fc37
--- /dev/null
+++ b/Audio_Recorder/g1_tts_arabic.py
@@ -0,0 +1,342 @@
+#!/usr/bin/env python3
+"""
+G1 Arabic TTS — Offline text-to-speech with playback on G1 built-in speaker.
+Supports Piper TTS (recommended) and tts_arabic as backends.
+
+Install on robot (gemini env):
+  pip install piper-tts
+  # First run auto-downloads Arabic voice model (~50MB)
+
+Usage (run ON the robot):
+  python3 g1_tts_arabic.py "مرحبا بكم في لوتاه"
+  python3 g1_tts_arabic.py "مرحبا" --save "/home/unitree/SanadVoice/recorded voices/marhaba.wav"
+  python3 g1_tts_arabic.py --interactive
+  python3 g1_tts_arabic.py "Hello welcome" --lang en
+  python3 g1_tts_arabic.py --list-voices
+"""
+
+import argparse
+import io
+import json
+import os
+import sys
+import time
+import wave
+import numpy as np
+
+TARGET_RATE = 16000
+DDS_IFACE = "eth0"
+
+
+def _init_g1_audio():
+    """Initialize G1 AudioClient."""
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+    ChannelFactoryInitialize(0, DDS_IFACE)
+    client = AudioClient()
+    client.SetTimeout(10.0)
+    client.Init()
+    client.SetVolume(100)
+    return client
+
+
+def _play_on_g1(client, audio_16k: np.ndarray):
+    """Play 16kHz mono int16 audio on G1 speaker."""
+    from unitree_sdk2py.g1.audio.g1_audio_api import (
+        ROBOT_API_ID_AUDIO_START_PLAY,
+        ROBOT_API_ID_AUDIO_STOP_PLAY,
+    )
+
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "tts"}))
+    time.sleep(0.3)
+
+    pcm = audio_16k.tobytes()
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": "tts",
+        "stream_id": sid,
+        "sample_rate": TARGET_RATE,
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+    client._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+
+    duration = len(audio_16k) / TARGET_RATE
+    time.sleep(duration + 0.5)
+    client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "tts"}))
+    return duration
+
+
+def _resample(audio: np.ndarray, src_rate: int) -> np.ndarray:
+    """Resample to 16kHz."""
+    if src_rate == TARGET_RATE:
+        return audio
+    tl = int(len(audio) * TARGET_RATE / src_rate)
+    return np.interp(
+        np.linspace(0, len(audio), tl, endpoint=False),
+        np.arange(len(audio)),
+        audio.astype(np.float64),
+    ).astype(np.int16)
+
+
+def _save_wav(path: str, audio: np.ndarray, rate: int = TARGET_RATE):
+    """Save audio as WAV file."""
+    wf = wave.open(path, "wb")
+    wf.setnchannels(1)
+    wf.setsampwidth(2)
+    wf.setframerate(rate)
+    wf.writeframes(audio.tobytes())
+    wf.close()
+
+
+# ─── PIPER TTS BACKEND ──────────────────────────────────────
+
+def tts_piper(text: str, voice: str = "ar_JO-kareem-medium", speaker: int = None):
+    """Generate speech using Piper TTS. Returns (audio_int16, sample_rate)."""
+    try:
+        import subprocess
+        # Use piper CLI — most reliable on ARM64
+        cmd = ["piper", "--model", voice, "--output_raw"]
+        if speaker is not None:
+            cmd += ["--speaker", str(speaker)]
+
+        proc = subprocess.run(
+            cmd, input=text.encode("utf-8"),
+            capture_output=True, timeout=30,
+        )
+
+        if proc.returncode != 0:
+            stderr = proc.stderr.decode()
+            # If model not found, try downloading
+            if "not found" in stderr.lower() or "No such file" in stderr:
+                print(f"  Downloading voice model '{voice}'...")
+                proc = subprocess.run(
+                    cmd, input=text.encode("utf-8"),
+                    capture_output=True, timeout=120,
+                )
+
+        if proc.returncode != 0:
+            raise RuntimeError(f"Piper error: {proc.stderr.decode()[:200]}")
+
+        # Piper outputs raw 16-bit mono PCM at 22050Hz by default
+        audio = np.frombuffer(proc.stdout, dtype=np.int16)
+        return audio, 22050
+
+    except FileNotFoundError:
+        raise RuntimeError("Piper not installed. Run: pip install piper-tts")
+
+
+def tts_piper_python(text: str, voice: str = "ar_JO-kareem-medium"):
+    """Generate speech using Piper Python API."""
+    try:
+        from piper import PiperVoice
+
+        model_path = os.path.expanduser(f"~/.local/share/piper-voices/{voice}.onnx")
+        if not os.path.exists(model_path):
+            # Try auto-download via CLI first
+            print(f"  Model not found at {model_path}")
+            print(f"  Run: piper --model {voice} --output_raw <<< 'test'")
+            raise FileNotFoundError(model_path)
+
+        pv = PiperVoice.load(model_path)
+        buf = io.BytesIO()
+        with wave.open(buf, "wb") as wf:
+            pv.synthesize(text, wf)
+
+        buf.seek(0)
+        with wave.open(buf, "rb") as wf:
+            rate = wf.getframerate()
+            audio = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
+
+        return audio, rate
+
+    except ImportError:
+        raise RuntimeError("Piper not installed. Run: pip install piper-tts")
+
+
+# ─── ESPEAK BACKEND (FALLBACK) ──────────────────────────────
+
+def tts_espeak(text: str, lang: str = "ar", speed: int = 130):
+    """Generate speech using espeak-ng. Low quality but always available."""
+    import subprocess
+
+    wav_path = "/tmp/_espeak_tts.wav"
+    cmd = ["espeak-ng", "-v", lang, "-s", str(speed), "-w", wav_path, text]
+    proc = subprocess.run(cmd, capture_output=True)
+
+    if proc.returncode != 0:
+        raise RuntimeError(f"espeak-ng error: {proc.stderr.decode()[:200]}")
+
+    with wave.open(wav_path, "rb") as wf:
+        rate = wf.getframerate()
+        audio = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
+
+    os.unlink(wav_path)
+    return audio, rate
+
+
+# ─── UNIFIED TTS ─────────────────────────────────────────────
+
+def synthesize(text: str, backend: str = "auto", lang: str = "ar", voice: str = None):
+    """Synthesize text to audio. Returns (audio_int16_16kHz, duration)."""
+
+    if backend == "auto":
+        # Try piper first, fall back to espeak
+        for try_backend in ["piper", "espeak"]:
+            try:
+                return synthesize(text, backend=try_backend, lang=lang, voice=voice)
+            except RuntimeError as e:
+                print(f"  {try_backend} failed: {e}")
+                continue
+        raise RuntimeError("No TTS backend available!")
+
+    if backend == "piper":
+        if voice is None:
+            voice = "ar_JO-kareem-medium" if lang == "ar" else "en_US-lessac-medium"
+        audio, rate = tts_piper(text, voice=voice)
+
+    elif backend == "espeak":
+        audio, rate = tts_espeak(text, lang=lang)
+
+    else:
+        raise ValueError(f"Unknown backend: {backend}")
+
+    # Resample to 16kHz
+    audio_16k = _resample(audio, rate)
+    duration = len(audio_16k) / TARGET_RATE
+
+    return audio_16k, duration
+
+
+# ─── COMMANDS ─────────────────────────────────────────────────
+
+def cmd_speak(args):
+    """Synthesize and play on G1."""
+    text = args.text
+    print(f'Text: "{text}"')
+    print(f"Backend: {args.backend}, lang: {args.lang}")
+
+    audio, duration = synthesize(text, backend=args.backend, lang=args.lang, voice=args.voice)
+    print(f"Generated: {duration:.1f}s")
+
+    if args.save:
+        _save_wav(args.save, audio)
+        print(f"Saved: {args.save}")
+
+    if not args.no_play:
+        print("Playing on G1...")
+        client = _init_g1_audio()
+        _play_on_g1(client, audio)
+        print("Done.")
+
+
+def cmd_interactive(args):
+    """Interactive Arabic TTS loop."""
+    print("G1 Arabic TTS — Interactive Mode")
+    print("Type Arabic (or English) text, press Enter to speak.")
+    print("Commands: /lang ar|en, /voice <name>, /backend piper|espeak, /quit\n")
+
+    client = _init_g1_audio()
+    lang = "ar"
+    backend = args.backend
+    voice = args.voice
+
+    while True:
+        try:
+            text = input(f"[{lang}] > ").strip()
+        except (KeyboardInterrupt, EOFError):
+            print("\nBye.")
+            break
+
+        if not text:
+            continue
+
+        if text.startswith("/"):
+            parts = text.split()
+            cmd = parts[0]
+            if cmd == "/quit":
+                break
+            elif cmd == "/lang" and len(parts) > 1:
+                lang = parts[1]
+                print(f"  Language: {lang}")
+            elif cmd == "/voice" and len(parts) > 1:
+                voice = parts[1]
+                print(f"  Voice: {voice}")
+            elif cmd == "/backend" and len(parts) > 1:
+                backend = parts[1]
+                print(f"  Backend: {backend}")
+            elif cmd == "/save" and len(parts) > 1:
+                save_path = " ".join(parts[1:])
+                print(f"  Next speech will be saved to: {save_path}")
+            else:
+                print("  Commands: /lang ar|en, /voice <name>, /backend piper|espeak, /quit")
+            continue
+
+        try:
+            audio, duration = synthesize(text, backend=backend, lang=lang, voice=voice)
+            print(f"  {duration:.1f}s — playing...", end=" ", flush=True)
+            _play_on_g1(client, audio)
+            print("done")
+        except Exception as e:
+            print(f"  Error: {e}")
+
+
+def cmd_list_voices(args):
+    """List available Piper voices."""
+    import subprocess
+
+    print("Piper Arabic voices:")
+    print("  ar_JO-kareem-low      — Jordanian Arabic (low quality, fastest)")
+    print("  ar_JO-kareem-medium   — Jordanian Arabic (medium quality, recommended)")
+    print()
+    print("Piper English voices:")
+    print("  en_US-lessac-medium   — US English male")
+    print("  en_US-amy-medium      — US English female")
+    print("  en_GB-alba-medium     — British English")
+    print()
+    print("espeak-ng Arabic voices:")
+    try:
+        result = subprocess.run(["espeak-ng", "--voices=ar"], capture_output=True, text=True)
+        print(result.stdout)
+    except FileNotFoundError:
+        print("  espeak-ng not installed")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="G1 Arabic TTS — Offline text-to-speech on G1 speaker",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s "مرحبا بكم في لوتاه"
+  %(prog)s "مرحبا" --save marhaba.wav
+  %(prog)s "Hello" --lang en
+  %(prog)s --interactive
+  %(prog)s --list-voices
+  %(prog)s "مرحبا" --backend espeak
+        """,
+    )
+    parser.add_argument("text", nargs="?", help="Text to speak")
+    parser.add_argument("--lang", "-l", default="ar", help="Language: ar, en (default: ar)")
+    parser.add_argument("--backend", "-b", default="auto", choices=["auto", "piper", "espeak"],
+                        help="TTS backend (default: auto)")
+    parser.add_argument("--voice", "-V", default=None, help="Piper voice name")
+    parser.add_argument("--save", "-s", default=None, help="Save WAV to path")
+    parser.add_argument("--no-play", action="store_true", help="Don't play on G1, just save")
+    parser.add_argument("--interactive", "-i", action="store_true", help="Interactive mode")
+    parser.add_argument("--list-voices", action="store_true", help="List available voices")
+
+    args = parser.parse_args()
+
+    if args.list_voices:
+        cmd_list_voices(args)
+    elif args.interactive:
+        cmd_interactive(args)
+    elif args.text:
+        cmd_speak(args)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/g1_tts_test.py b/Audio_Recorder/g1_tts_test.py
new file mode 100644
index 0000000..07b20e7
--- /dev/null
+++ b/Audio_Recorder/g1_tts_test.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""Test TTS on G1 built-in speaker — Arabic, English, Chinese."""
+
+import sys
+import time
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+
+if len(sys.argv) < 2:
+    print(f"Usage: python3 {sys.argv[0]} <network_interface>")
+    print(f"  e.g. python3 {sys.argv[0]} eth0")
+    sys.exit(1)
+
+ChannelFactoryInitialize(0, sys.argv[1])
+c = AudioClient()
+c.SetTimeout(10.0)
+c.Init()
+c.SetVolume(100)
+
+tests = [
+    ("English", 0, "Hello, I am the Unitree G1 robot. Welcome to Lootah."),
+    ("English", 0, "How are you today? I hope you are doing well."),
+    ("Chinese", 0, "你好，我是宇树科技的人形机器人。欢迎光临。"),
+    ("Arabic", 0, "مرحبا، أنا الروبوت يونيتري جي وان. أهلا وسهلا بكم."),
+    ("Arabic", 0, "كيف حالك اليوم؟ أتمنى أن تكون بخير."),
+    ("English", 0, "Testing numbers: one, two, three, four, five."),
+    ("Arabic", 0, "واحد، اثنان، ثلاثة، أربعة، خمسة."),
+]
+
+for lang, speaker_id, text in tests:
+    print(f"\n[{lang}] speaker_id={speaker_id}")
+    print(f"  Text: {text}")
+    code = c.TtsMaker(text, speaker_id)
+    print(f"  Return code: {code}")
+    # Wait proportional to text length
+    wait = max(3, len(text) * 0.08)
+    print(f"  Waiting {wait:.1f}s...")
+    time.sleep(wait)
+
+# Try different speaker IDs for English
+print("\n--- Testing different speaker IDs ---")
+for sid in [0, 1, 2]:
+    text = "Hello, testing speaker ID."
+    print(f"\n  speaker_id={sid}: '{text}'")
+    code = c.TtsMaker(text, sid)
+    print(f"  code={code}")
+    time.sleep(4)
+
+print("\nDone. Which languages did you hear?")
diff --git a/Audio_Recorder/g1_voice_deploy.py b/Audio_Recorder/g1_voice_deploy.py
new file mode 100644
index 0000000..04b7a5a
--- /dev/null
+++ b/Audio_Recorder/g1_voice_deploy.py
@@ -0,0 +1,479 @@
+#!/usr/bin/env python3
+"""
+G1 Voice Deploy — Record on workstation, deploy & play on G1 robot.
+Also sets up mic on robot and manages remote recordings.
+
+Usage (run from workstation):
+  python3 g1_voice_deploy.py setup-mic          # Unmute & configure wireless mic on robot
+  python3 g1_voice_deploy.py record --name hi    # Record from workstation mic, save locally
+  python3 g1_voice_deploy.py record-robot --name arabic_greeting --seconds 5  # Record from robot mic
+  python3 g1_voice_deploy.py play --name hi      # Deploy & play on G1 speaker
+  python3 g1_voice_deploy.py play-robot --path "/home/unitree/SanadVoice/recorded voices/welcome_single.wav"
+  python3 g1_voice_deploy.py tts --text "Hello"  # TTS on G1 speaker
+  python3 g1_voice_deploy.py list                # List local recordings
+  python3 g1_voice_deploy.py list-robot          # List recordings on robot
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+import wave
+import numpy as np
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+DATA_DIR = SCRIPT_DIR / "DataG1"
+
+ROBOT_IP = "192.168.123.164"
+ROBOT_USER = "unitree"
+ROBOT_PYTHON = "/home/unitree/miniconda3/envs/gemini/bin/python3"
+ROBOT_VOICES_DIR = "/home/unitree/SanadVoice/recorded voices"
+ROBOT_DDS_IFACE = "eth0"
+
+SAMPLE_RATE = 16000
+CHANNELS = 1
+SAMPLE_WIDTH = 2
+CHUNK_SIZE = 4096
+
+SSH_OPTS = ["-o", "StrictHostKeyChecking=no", "-o", "ConnectTimeout=5"]
+
+
+def ssh_cmd(cmd: str, capture=True):
+    """Run a command on the robot via SSH."""
+    target = f"{ROBOT_USER}@{ROBOT_IP}"
+    result = subprocess.run(
+        ["ssh"] + SSH_OPTS + [target, cmd],
+        capture_output=capture, text=True,
+    )
+    if capture:
+        return result.returncode, result.stdout.strip(), result.stderr.strip()
+    return result.returncode, "", ""
+
+
+def scp_to_robot(local_path: str, remote_path: str):
+    """Copy a file to the robot."""
+    target = f"{ROBOT_USER}@{ROBOT_IP}"
+    ret = subprocess.run(
+        ["scp"] + SSH_OPTS + [local_path, f"{target}:{remote_path}"],
+        capture_output=True, text=True,
+    )
+    return ret.returncode == 0
+
+
+def scp_from_robot(remote_path: str, local_path: str):
+    """Copy a file from the robot."""
+    target = f"{ROBOT_USER}@{ROBOT_IP}"
+    ret = subprocess.run(
+        ["scp"] + SSH_OPTS + [f"{target}:{remote_path}", local_path],
+        capture_output=True, text=True,
+    )
+    return ret.returncode == 0
+
+
+# ─── SETUP MIC ──────────────────────────────────────────────
+
+def cmd_setup_mic(args):
+    """Unmute and configure the wireless mic on the robot."""
+    print("Setting up wireless mic on G1...\n")
+
+    # List sources
+    code, out, _ = ssh_cmd("pactl list sources short")
+    print("PulseAudio sources:")
+    print(out)
+    print()
+
+    # Find wireless mic source
+    mic_source = None
+    for line in out.split("\n"):
+        if "Wireless" in line or "Hollyland" in line:
+            mic_source = line.split("\t")[1] if "\t" in line else None
+            mic_index = line.split("\t")[0] if "\t" in line else None
+            break
+
+    if not mic_source:
+        print("Wireless mic not found. Is it plugged in?")
+        return
+
+    print(f"Found mic: {mic_source} (index {mic_index})")
+
+    # Set as default, unmute, set volume
+    ssh_cmd(f"pactl set-default-source {mic_source}")
+    print("  Set as default source.")
+
+    ssh_cmd(f"pactl set-source-mute {mic_index} 0")
+    print("  Unmuted.")
+
+    ssh_cmd(f"pactl set-source-volume {mic_index} 100%")
+    print("  Volume set to 100%.")
+
+    # Check mute status
+    code, out, _ = ssh_cmd(f'pactl list sources | grep -A 3 "Wireless" | grep Mute')
+    print(f"  Status: {out.strip()}")
+
+    # Verify recording
+    print("\n  Verifying mic (2 second test)...")
+    ssh_cmd("timeout 2 parec -d 3 --format=s16le --rate=16000 --channels=1 --raw > /tmp/_mic_verify.pcm")
+    code, out, _ = ssh_cmd(
+        f'{ROBOT_PYTHON} -c "'
+        'import numpy as np;'
+        'a = np.fromfile(\'/tmp/_mic_verify.pcm\', dtype=np.int16);'
+        'print(f\'samples={len(a)} std={a.std():.0f}\')'
+        '"'
+    )
+    print(f"  Result: {out}")
+    if "std=0" in out:
+        print("  WARNING: Mic is silent! Check transmitter is powered on.")
+    else:
+        print("  Mic is working!")
+
+
+# ─── RECORD ON WORKSTATION ───────────────────────────────────
+
+def cmd_record(args):
+    """Record from workstation microphone."""
+    import pyaudio
+
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    out_path = DATA_DIR / f"{args.name}.wav"
+
+    pa = pyaudio.PyAudio()
+
+    # Find best input device
+    device_index = args.device
+    if device_index is None:
+        for i in range(pa.get_device_count()):
+            info = pa.get_device_info_by_index(i)
+            if info["maxInputChannels"] > 0:
+                name = info["name"]
+                if name in ("default", "pipewire") or "pulse" in name.lower():
+                    device_index = i
+                    break
+        if device_index is None:
+            for i in range(pa.get_device_count()):
+                if pa.get_device_info_by_index(i)["maxInputChannels"] > 0:
+                    device_index = i
+                    break
+
+    # Find supported rate
+    info = pa.get_device_info_by_index(device_index)
+    rec_rate = int(info["defaultSampleRate"])
+    print(f"Using device [{device_index}] {info['name']} @ {rec_rate}Hz")
+    print(f"Recording {args.seconds}s... speak now!\n")
+
+    stream = pa.open(
+        format=pyaudio.paInt16, channels=1, rate=rec_rate,
+        input=True, input_device_index=device_index, frames_per_buffer=CHUNK_SIZE,
+    )
+
+    frames = []
+    total = int(rec_rate / CHUNK_SIZE * args.seconds)
+    start = time.time()
+    for i in range(total):
+        frames.append(stream.read(CHUNK_SIZE, exception_on_overflow=False))
+        elapsed = time.time() - start
+        pct = min(elapsed / args.seconds, 1.0)
+        bar = "█" * int(30 * pct) + "░" * (30 - int(30 * pct))
+        print(f"\r  [{bar}] {elapsed:.1f}s / {args.seconds:.1f}s", end="", flush=True)
+
+    stream.stop_stream()
+    stream.close()
+    pa.terminate()
+    print()
+
+    # Resample to 16kHz
+    audio = np.frombuffer(b"".join(frames), dtype=np.int16)
+    if rec_rate != SAMPLE_RATE:
+        tl = int(len(audio) * SAMPLE_RATE / rec_rate)
+        audio = np.interp(
+            np.linspace(0, len(audio), tl, endpoint=False),
+            np.arange(len(audio)), audio.astype(np.float64),
+        ).astype(np.int16)
+
+    wf = wave.open(str(out_path), "wb")
+    wf.setnchannels(1)
+    wf.setsampwidth(2)
+    wf.setframerate(SAMPLE_RATE)
+    wf.writeframes(audio.tobytes())
+    wf.close()
+
+    print(f"Saved: {out_path} ({len(audio)/SAMPLE_RATE:.1f}s, {out_path.stat().st_size/1024:.1f}KB)")
+
+
+# ─── RECORD ON ROBOT ─────────────────────────────────────────
+
+def cmd_record_robot(args):
+    """Record from the wireless mic on the robot."""
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    seconds = args.seconds
+    remote_wav = f"/tmp/_rec_{args.name}.wav"
+    local_path = DATA_DIR / f"{args.name}.wav"
+
+    record_code = (
+        f"import time, subprocess, wave, numpy as np;"
+        f"print('Recording {seconds}s... speak now!');"
+        f"proc = subprocess.Popen(['parec','-d','3','--format=s16le','--rate=16000','--channels=1','--raw'], stdout=subprocess.PIPE);"
+        f"time.sleep({seconds});"
+        f"proc.terminate();"
+        f"raw = proc.stdout.read();"
+        f"audio = np.frombuffer(raw, dtype=np.int16);"
+        f"print(f'Recorded {{len(audio)}} samples, std={{audio.std():.0f}}');"
+        f"wf = wave.open('{remote_wav}', 'wb');"
+        f"wf.setnchannels(1); wf.setsampwidth(2); wf.setframerate(16000);"
+        f"wf.writeframes(audio.tobytes()); wf.close();"
+        f"print('Saved on robot')"
+    )
+
+    print(f"Recording {seconds}s from robot wireless mic...")
+    code, out, err = ssh_cmd(f'{ROBOT_PYTHON} -c "{record_code}"')
+    print(f"  {out}")
+    if err:
+        print(f"  stderr: {err}")
+
+    # Download to workstation
+    print(f"Downloading to {local_path}...")
+    if scp_from_robot(remote_wav, str(local_path)):
+        print(f"  Saved: {local_path}")
+        ssh_cmd(f"rm -f {remote_wav}")
+    else:
+        print("  Download failed!")
+
+    # Also save on robot in recorded voices dir
+    remote_final = f"{ROBOT_VOICES_DIR}/{args.name}.wav"
+    ssh_cmd(f'cp "{remote_wav}" "{remote_final}" 2>/dev/null')
+
+
+# ─── PLAY ON G1 ──────────────────────────────────────────────
+
+def cmd_play(args):
+    """Deploy a local WAV file to G1 and play on built-in speaker."""
+    wav_path = DATA_DIR / f"{args.name}.wav"
+    if not wav_path.exists():
+        print(f"Not found: {wav_path}")
+        cmd_list(args)
+        return
+
+    with wave.open(str(wav_path), "rb") as wf:
+        dur = wf.getnframes() / wf.getframerate()
+    print(f"Loaded: {wav_path.name} ({dur:.1f}s)")
+
+    # SCP to robot
+    remote_wav = f"/tmp/_play_{args.name}.wav"
+    print(f"Copying to robot...")
+    if not scp_to_robot(str(wav_path), remote_wav):
+        print("  SCP failed!")
+        return
+    print("  Copied.")
+
+    # Play on robot
+    _play_remote_wav(remote_wav, dur)
+
+    # Cleanup
+    ssh_cmd(f"rm -f {remote_wav}")
+
+
+def cmd_play_robot(args):
+    """Play a WAV file that already exists on the robot."""
+    # Get duration
+    code, out, _ = ssh_cmd(
+        f'{ROBOT_PYTHON} -c "import wave; '
+        f"wf = wave.open('{args.path}', 'rb'); "
+        f"print(wf.getnframes() / wf.getframerate()); wf.close()"
+        '"'
+    )
+    try:
+        dur = float(out)
+    except ValueError:
+        print(f"Cannot read: {args.path}")
+        print(f"  {out}")
+        return
+
+    print(f"Playing: {args.path} ({dur:.1f}s)")
+    _play_remote_wav(args.path, dur)
+
+
+def _play_remote_wav(remote_wav: str, duration: float):
+    """Play a WAV file on the G1 speaker (file must already be on robot)."""
+    play_code = (
+        "import time, wave, json, numpy as np;"
+        "from unitree_sdk2py.core.channel import ChannelFactoryInitialize;"
+        "from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient;"
+        "from unitree_sdk2py.g1.audio.g1_audio_api import *;"
+        f"ChannelFactoryInitialize(0, '{ROBOT_DDS_IFACE}');"
+        "c = AudioClient(); c.SetTimeout(10.0); c.Init(); c.SetVolume(100);"
+        "c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({'app_name':'p'}));"
+        "time.sleep(0.3);"
+        f"wf = wave.open('{remote_wav}', 'rb');"
+        "rate = wf.getframerate(); nch = wf.getnchannels();"
+        "audio = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16); wf.close();"
+        "audio = audio.reshape(-1, 2).mean(axis=1).astype(np.int16) if nch == 2 else audio;"
+        "tl = int(len(audio) * 16000 / rate) if rate != 16000 else len(audio);"
+        "audio = np.interp(np.linspace(0, len(audio), tl, endpoint=False), np.arange(len(audio)), audio.astype(np.float64)).astype(np.int16) if rate != 16000 else audio;"
+        "pcm = audio.tobytes();"
+        "sid = f's_{int(time.time()*1000)}';"
+        "param = json.dumps({'app_name':'p','stream_id':sid,'sample_rate':16000,'channels':1,'bits_per_sample':16});"
+        "c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm));"
+        f"time.sleep({duration + 1});"
+        "c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({'app_name':'p'}));"
+        "print('played')"
+    )
+
+    print(f"Playing on G1... ({duration:.1f}s)")
+    proc = subprocess.Popen(
+        ["ssh"] + SSH_OPTS + [f"{ROBOT_USER}@{ROBOT_IP}",
+         f'{ROBOT_PYTHON} -c "{play_code}"'],
+        stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
+    )
+
+    start = time.time()
+    while proc.poll() is None:
+        elapsed = time.time() - start
+        pct = min(elapsed / duration, 1.0)
+        bar = "█" * int(30 * pct) + "░" * (30 - int(30 * pct))
+        print(f"\r  [{bar}] {min(elapsed, duration):.1f}s / {duration:.1f}s", end="", flush=True)
+        time.sleep(0.2)
+
+    out = proc.stdout.read().decode().strip()
+    if "played" in out:
+        print(f"\n  Done.")
+    else:
+        print(f"\n  Robot output: {out}")
+
+
+# ─── TTS ──────────────────────────────────────────────────────
+
+def cmd_tts(args):
+    """Play text-to-speech on G1 speaker (English/Chinese only)."""
+    tts_code = (
+        "import json;"
+        "from unitree_sdk2py.core.channel import ChannelFactoryInitialize;"
+        "from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient;"
+        f"ChannelFactoryInitialize(0, '{ROBOT_DDS_IFACE}');"
+        "c = AudioClient(); c.SetTimeout(10.0); c.Init();"
+        f"c.SetVolume({args.volume});"
+        f"c.TtsMaker('{args.text}', {args.speaker_id});"
+        "print('ok')"
+    )
+    print(f"TTS: \"{args.text}\" (speaker_id={args.speaker_id}, vol={args.volume})")
+    code, out, _ = ssh_cmd(f'{ROBOT_PYTHON} -c "{tts_code}"')
+    if "ok" in out:
+        print("  Sent.")
+    else:
+        print(f"  Result: {out}")
+
+
+# ─── LIST ─────────────────────────────────────────────────────
+
+def cmd_list(args):
+    """List local recordings."""
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    wavs = sorted(DATA_DIR.glob("*.wav"))
+    if not wavs:
+        print(f"No recordings in {DATA_DIR}")
+        return
+    print(f"\n{'#':<4} {'Name':<35} {'Duration':>8} {'Size':>10}")
+    print("-" * 60)
+    for i, p in enumerate(wavs):
+        try:
+            with wave.open(str(p), "rb") as wf:
+                dur = wf.getnframes() / wf.getframerate()
+            size = p.stat().st_size / 1024
+            print(f"{i:<4} {p.stem:<35} {dur:>6.1f}s {size:>8.1f}KB")
+        except Exception:
+            print(f"{i:<4} {p.stem:<35} {'ERROR':>8}")
+    print()
+
+
+def cmd_list_robot(args):
+    """List recordings on the robot."""
+    code, out, _ = ssh_cmd(
+        f'{ROBOT_PYTHON} -c "'
+        "import wave, os; from pathlib import Path;"
+        f"d = Path('{ROBOT_VOICES_DIR}');"
+        "wavs = sorted(d.glob('*.wav'));"
+        "print(f'{{len(wavs)}} files in {d}');"
+        "for p in wavs:"
+        "    try:"
+        "        wf = wave.open(str(p),'rb'); dur = wf.getnframes()/wf.getframerate(); wf.close();"
+        "        print(f'{p.stem:<45} {dur:>6.1f}s')"
+        "    except: print(f'{p.stem:<45} ERROR')"
+        '"'
+    )
+    print(out)
+
+
+# ─── MAIN ─────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="G1 Voice Deploy — Record, deploy & play audio on G1 robot",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s setup-mic
+  %(prog)s record --name greeting --seconds 5
+  %(prog)s record-robot --name arabic_hi --seconds 5
+  %(prog)s play --name greeting
+  %(prog)s play-robot --path "/home/unitree/SanadVoice/recorded voices/welcome_single.wav"
+  %(prog)s tts --text "Hello, welcome!"
+  %(prog)s list
+  %(prog)s list-robot
+        """,
+    )
+
+    sub = parser.add_subparsers(dest="command")
+
+    # setup-mic
+    sub.add_parser("setup-mic", help="Unmute & configure wireless mic on robot")
+
+    # record
+    p = sub.add_parser("record", help="Record from workstation mic")
+    p.add_argument("--name", "-n", required=True, help="Recording name")
+    p.add_argument("--seconds", "-s", type=float, default=5.0)
+    p.add_argument("--device", "-d", type=int, default=None)
+
+    # record-robot
+    p = sub.add_parser("record-robot", help="Record from robot wireless mic")
+    p.add_argument("--name", "-n", required=True, help="Recording name")
+    p.add_argument("--seconds", "-s", type=float, default=5.0)
+
+    # play
+    p = sub.add_parser("play", help="Deploy & play local WAV on G1 speaker")
+    p.add_argument("--name", "-n", required=True, help="Recording name")
+
+    # play-robot
+    p = sub.add_parser("play-robot", help="Play a WAV file already on robot")
+    p.add_argument("--path", "-p", required=True, help="Full path on robot")
+
+    # tts
+    p = sub.add_parser("tts", help="Text-to-speech on G1 (English/Chinese)")
+    p.add_argument("--text", "-t", required=True, help="Text to speak")
+    p.add_argument("--speaker-id", type=int, default=0, help="Voice ID (0,1,2)")
+    p.add_argument("--volume", "-v", type=int, default=100)
+
+    # list
+    sub.add_parser("list", help="List local recordings")
+    sub.add_parser("list-robot", help="List recordings on robot")
+
+    args = parser.parse_args()
+
+    commands = {
+        "setup-mic": cmd_setup_mic,
+        "record": cmd_record,
+        "record-robot": cmd_record_robot,
+        "play": cmd_play,
+        "play-robot": cmd_play_robot,
+        "tts": cmd_tts,
+        "list": cmd_list,
+        "list-robot": cmd_list_robot,
+    }
+
+    if args.command in commands:
+        commands[args.command](args)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/g1_voice_recorder.py b/Audio_Recorder/g1_voice_recorder.py
new file mode 100644
index 0000000..e773a52
--- /dev/null
+++ b/Audio_Recorder/g1_voice_recorder.py
@@ -0,0 +1,463 @@
+#!/usr/bin/env python3
+"""
+G1 VOICE RECORDER & REPLAYER
+-----------------------------
+Records voice from laptop microphone, saves as WAV, and replays
+on the Unitree G1 robot via AudioClient.PlayStream().
+
+Modes:
+  record   - Record from mic and save to DataG1/<name>.wav
+  replay   - Play a saved WAV file on G1 speaker
+  list     - List all saved recordings
+  live     - Record then immediately replay on G1
+
+Audio Format:
+  16-bit PCM, 16000 Hz, Mono (G1 voice service expects this)
+
+Usage:
+  python3 g1_voice_recorder.py enp3s0 record  --name greeting --seconds 5
+  python3 g1_voice_recorder.py enp3s0 replay  --name greeting
+  python3 g1_voice_recorder.py enp3s0 list
+  python3 g1_voice_recorder.py enp3s0 live    --seconds 5
+"""
+
+import time
+import sys
+import wave
+import argparse
+import numpy as np
+import os
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+G1_LOOTAH_DIR = SCRIPT_DIR.parent
+DATA_DIR = SCRIPT_DIR / "DataG1"
+
+# Add unitree_webrtc_connect to path
+import sys
+_webrtc_path = str(G1_LOOTAH_DIR / "unitree_webrtc_connect")
+if _webrtc_path not in sys.path:
+    sys.path.insert(0, _webrtc_path)
+
+# G1 AudioClient PCM format
+SAMPLE_RATE = 16000
+CHANNELS = 1
+SAMPLE_WIDTH = 2  # 16-bit
+CHUNK_SIZE = 4096  # frames per buffer
+
+
+def list_recordings():
+    """List all saved WAV recordings."""
+    wavs = sorted(DATA_DIR.glob("*.wav"))
+    if not wavs:
+        print("No recordings found in", DATA_DIR)
+        return []
+
+    print(f"\n{'Name':<30} {'Duration':>10} {'Size':>10}")
+    print("-" * 52)
+    for p in wavs:
+        try:
+            with wave.open(str(p), "rb") as wf:
+                frames = wf.getnframes()
+                rate = wf.getframerate()
+                dur = frames / rate
+            size_kb = p.stat().st_size / 1024
+            print(f"{p.stem:<30} {dur:>8.1f}s {size_kb:>8.1f}KB")
+        except Exception as e:
+            print(f"{p.stem:<30} {'ERROR':>10} {str(e)}")
+    print()
+    return wavs
+
+
+def _find_supported_rate(pa, device_index: int):
+    """Find a sample rate the device actually supports."""
+    import pyaudio
+    info = pa.get_device_info_by_index(device_index)
+    # Try common rates, prefer the device's default first
+    candidates = [int(info["defaultSampleRate"]), 48000, 44100, 32000, 16000, 8000]
+    for rate in candidates:
+        try:
+            supported = pa.is_format_supported(
+                rate, input_device=device_index,
+                input_channels=1, input_format=pyaudio.paInt16,
+            )
+            if supported:
+                return rate
+        except ValueError:
+            continue
+    # Fallback to device default
+    return int(info["defaultSampleRate"])
+
+
+def record_voice(name: str, seconds: float, device_index: int = None):
+    """Record audio from microphone and save as WAV."""
+    import pyaudio
+
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+    out_path = DATA_DIR / f"{name}.wav"
+
+    pa = pyaudio.PyAudio()
+
+    # Show available input devices — prefer 'default' or 'pipewire' over raw hw:
+    if device_index is None:
+        print("\nAvailable input devices:")
+        best_idx = None
+        for i in range(pa.get_device_count()):
+            info = pa.get_device_info_by_index(i)
+            if info["maxInputChannels"] > 0:
+                dname = info["name"]
+                marker = ""
+                # Prefer 'default' or 'pipewire' — they handle mixing/resampling properly
+                if best_idx is None:
+                    if dname in ("default", "pipewire") or "pulse" in dname.lower():
+                        best_idx = i
+                        marker = " [SELECTED]"
+                elif i == best_idx:
+                    marker = " [SELECTED]"
+                print(f"  [{i}] {dname} (in={info['maxInputChannels']}, "
+                      f"default={int(info['defaultSampleRate'])}Hz){marker}")
+        # If no preferred device found, fall back to first available
+        if best_idx is None:
+            for i in range(pa.get_device_count()):
+                if pa.get_device_info_by_index(i)["maxInputChannels"] > 0:
+                    best_idx = i
+                    break
+        device_index = best_idx
+        print(f"\nUsing device [{device_index}] — {pa.get_device_info_by_index(device_index)['name']}")
+
+    # Find a sample rate the hardware supports
+    rec_rate = _find_supported_rate(pa, device_index)
+    needs_resample = rec_rate != SAMPLE_RATE
+
+    print(f"\nRecording: {seconds}s @ {rec_rate}Hz mono 16-bit"
+          + (f" (will resample to {SAMPLE_RATE}Hz)" if needs_resample else ""))
+    print(f"Output:    {out_path}")
+    print("Press Ctrl+C to stop early.\n")
+
+    stream = pa.open(
+        format=pyaudio.paInt16,
+        channels=CHANNELS,
+        rate=rec_rate,
+        input=True,
+        input_device_index=device_index,
+        frames_per_buffer=CHUNK_SIZE,
+    )
+
+    frames = []
+    total_chunks = int(rec_rate / CHUNK_SIZE * seconds)
+    start = time.time()
+
+    try:
+        for i in range(total_chunks):
+            data = stream.read(CHUNK_SIZE, exception_on_overflow=False)
+            frames.append(data)
+
+            elapsed = time.time() - start
+            bar_len = 30
+            progress = min(elapsed / seconds, 1.0)
+            filled = int(bar_len * progress)
+            bar = "█" * filled + "░" * (bar_len - filled)
+            print(f"\r  [{bar}] {elapsed:.1f}s / {seconds:.1f}s", end="", flush=True)
+
+    except KeyboardInterrupt:
+        print("\n  Recording stopped early.")
+
+    print()
+
+    stream.stop_stream()
+    stream.close()
+    pa.terminate()
+
+    # Convert raw frames to numpy
+    raw_audio = np.frombuffer(b"".join(frames), dtype=np.int16)
+
+    # Resample to G1 target rate if needed
+    if needs_resample:
+        target_len = int(len(raw_audio) * SAMPLE_RATE / rec_rate)
+        raw_audio = np.interp(
+            np.linspace(0, len(raw_audio), target_len, endpoint=False),
+            np.arange(len(raw_audio)),
+            raw_audio.astype(np.float64),
+        ).astype(np.int16)
+        print(f"  Resampled: {rec_rate}Hz -> {SAMPLE_RATE}Hz")
+
+    # Save WAV at G1 target rate
+    wf = wave.open(str(out_path), "wb")
+    wf.setnchannels(CHANNELS)
+    wf.setsampwidth(SAMPLE_WIDTH)
+    wf.setframerate(SAMPLE_RATE)
+    wf.writeframes(raw_audio.tobytes())
+    wf.close()
+
+    duration = len(raw_audio) / SAMPLE_RATE
+    size_kb = out_path.stat().st_size / 1024
+    print(f"Saved: {out_path.name} ({duration:.1f}s, {size_kb:.1f}KB)")
+    return out_path
+
+
+def replay_on_g1(name: str, robot_ip: str, user: str = "unitree"):
+    """Replay a saved WAV file on the G1 built-in speaker.
+
+    SCP the WAV + a tiny player script to the robot, then SSH and run it
+    using the robot's SDK which has AudioClient.PlayStream().
+    """
+    import subprocess
+
+    wav_path = DATA_DIR / f"{name}.wav"
+    if not wav_path.exists():
+        print(f"Recording not found: {wav_path}")
+        print("Available recordings:")
+        list_recordings()
+        sys.exit(1)
+
+    # Read WAV info
+    with wave.open(str(wav_path), "rb") as wf:
+        framerate = wf.getframerate()
+        n_channels = wf.getnchannels()
+        sampwidth = wf.getsampwidth()
+        n_frames = wf.getnframes()
+
+    duration = n_frames / framerate
+    print(f"\nLoaded: {wav_path.name}")
+    print(f"  Format:   {framerate}Hz, {n_channels}ch, {sampwidth * 8}-bit")
+    print(f"  Duration: {duration:.1f}s")
+
+    remote_wav = f"/tmp/{wav_path.name}"
+    remote_script = "/tmp/_g1_play_wav.py"
+    target = f"{user}@{robot_ip}"
+    ssh_opts = ["-o", "StrictHostKeyChecking=no", "-o", "ConnectTimeout=5"]
+
+    # Player script that runs ON the robot — uses _CallRequestWithParamAndBin
+    # (single call, unique stream_id, format params in JSON). PlayStream is
+    # unreliable — see voice_note.txt.
+    player_script = '''\
+import sys, time, wave, json, numpy as np
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+from unitree_sdk2py.g1.audio.g1_audio_api import (
+    ROBOT_API_ID_AUDIO_START_PLAY, ROBOT_API_ID_AUDIO_STOP_PLAY,
+)
+
+TARGET_RATE = 16000  # G1 speaker expects 16kHz mono 16-bit
+
+wav_path = sys.argv[1]
+ChannelFactoryInitialize(0, "eth0")
+client = AudioClient()
+client.SetTimeout(10.0)
+client.Init()
+client.SetVolume(100)
+
+with wave.open(wav_path, "rb") as wf:
+    rate = wf.getframerate()
+    nch = wf.getnchannels()
+    pcm_raw = wf.readframes(wf.getnframes())
+
+audio = np.frombuffer(pcm_raw, dtype=np.int16)
+if nch == 2:
+    audio = audio.reshape(-1, 2).mean(axis=1).astype(np.int16)
+if rate != TARGET_RATE:
+    target_len = int(len(audio) * TARGET_RATE / rate)
+    audio = np.interp(
+        np.linspace(0, len(audio), target_len, endpoint=False),
+        np.arange(len(audio)),
+        audio.astype(np.float64),
+    ).astype(np.int16)
+
+pcm_bytes = audio.tobytes()
+duration = len(audio) / TARGET_RATE
+
+# Reset any previous stream, then send ALL audio in ONE call with a unique sid.
+client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "voice_rec"}))
+time.sleep(0.3)
+
+sid = f"s_{int(time.time() * 1000)}"
+param = json.dumps({
+    "app_name": "voice_rec",
+    "stream_id": sid,
+    "sample_rate": TARGET_RATE,
+    "channels": 1,
+    "bits_per_sample": 16,
+})
+
+print(f"Playing {duration:.1f}s via _CallRequestWithParamAndBin (16kHz mono)...")
+start = time.time()
+client._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm_bytes))
+time.sleep(duration + 0.5)
+client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "voice_rec"}))
+print(f"Done. ({time.time() - start:.1f}s)")
+'''
+
+    # Step 1: Copy WAV + player script to robot
+    print(f"\nCopying to {target}...")
+
+    # Write player script to local temp file
+    import tempfile
+    local_script = os.path.join(tempfile.gettempdir(), "_g1_play_wav.py")
+    with open(local_script, "w") as f:
+        f.write(player_script)
+
+    # SCP WAV file
+    ret = subprocess.run(
+        ["scp"] + ssh_opts + [str(wav_path), f"{target}:{remote_wav}"],
+        capture_output=True, text=True,
+    )
+    if ret.returncode != 0:
+        print(f"  SCP WAV failed: {ret.stderr.strip()}")
+        os.unlink(local_script)
+        sys.exit(1)
+
+    # SCP player script
+    ret = subprocess.run(
+        ["scp"] + ssh_opts + [local_script, f"{target}:{remote_script}"],
+        capture_output=True, text=True,
+    )
+    os.unlink(local_script)
+
+    if ret.returncode != 0:
+        print(f"  SCP script failed: {ret.stderr.strip()}")
+        sys.exit(1)
+    print("  Copied.")
+
+    # Step 2: Run player script on robot via SSH
+    # Use full path to gemini env python (conda run fails in non-interactive SSH)
+    play_cmd = (
+        f"/home/unitree/miniconda3/envs/gemini/bin/python3 "
+        f"{remote_script} {remote_wav}"
+    )
+
+    print(f"Playing on G1... ({duration:.1f}s)")
+    proc = subprocess.Popen(
+        ["ssh"] + ssh_opts + [target, play_cmd],
+        stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
+    )
+
+    start = time.time()
+    while proc.poll() is None:
+        elapsed = time.time() - start
+        progress = min(elapsed / duration, 1.0)
+        bar_len = 30
+        filled = int(bar_len * progress)
+        bar = "█" * filled + "░" * (bar_len - filled)
+        print(f"\r  [{bar}] {min(elapsed, duration):.1f}s / {duration:.1f}s",
+              end="", flush=True)
+        time.sleep(0.2)
+
+    output = proc.stdout.read().decode().strip()
+    if output:
+        print(f"\n  Robot: {output}")
+
+    if proc.returncode != 0:
+        print(f"\n  SSH exit code: {proc.returncode}")
+    else:
+        print(f"\nPlayback complete.")
+
+    # Cleanup
+    subprocess.run(
+        ["ssh"] + ssh_opts + [target, f"rm -f {remote_wav} {remote_script}"],
+        capture_output=True,
+    )
+
+
+def live_record_and_replay(seconds: float, robot_ip: str, device_index: int = None, user: str = "unitree"):
+    """Record voice then immediately replay on G1."""
+    name = f"live_{int(time.time())}"
+    print("=== STEP 1: RECORD ===")
+    record_voice(name, seconds, device_index)
+
+    print("\n=== STEP 2: REPLAY ON G1 ===")
+    replay_on_g1(name, robot_ip, user)
+
+
+def play_local(name: str):
+    """Play a saved WAV file on the local workstation speaker."""
+    import pyaudio
+
+    wav_path = DATA_DIR / f"{name}.wav"
+    if not wav_path.exists():
+        print(f"Recording not found: {wav_path}")
+        list_recordings()
+        sys.exit(1)
+
+    with wave.open(str(wav_path), "rb") as wf:
+        n_channels = wf.getnchannels()
+        sampwidth = wf.getsampwidth()
+        framerate = wf.getframerate()
+        n_frames = wf.getnframes()
+
+        duration = n_frames / framerate
+        print(f"\nPlaying locally: {wav_path.name}")
+        print(f"  Format:   {framerate}Hz, {n_channels}ch, {sampwidth * 8}-bit")
+        print(f"  Duration: {duration:.1f}s\n")
+
+        pa = pyaudio.PyAudio()
+        stream = pa.open(
+            format=pa.get_format_from_width(sampwidth),
+            channels=n_channels,
+            rate=framerate,
+            output=True,
+        )
+
+        start = time.time()
+        chunk = wf.readframes(CHUNK_SIZE)
+        while chunk:
+            stream.write(chunk)
+            elapsed = time.time() - start
+            progress = min(elapsed / duration, 1.0)
+            bar_len = 30
+            filled = int(bar_len * progress)
+            bar = "█" * filled + "░" * (bar_len - filled)
+            print(f"\r  [{bar}] {elapsed:.1f}s / {duration:.1f}s", end="", flush=True)
+            chunk = wf.readframes(CHUNK_SIZE)
+
+        print(f"\n  Done.")
+        stream.stop_stream()
+        stream.close()
+        pa.terminate()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="G1 Voice Recorder & Replayer",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s record    --name hello --seconds 5
+  %(prog)s playlocal --name hello
+  %(prog)s replay    --name hello --ip 192.168.123.164
+  %(prog)s list
+  %(prog)s live      --seconds 5 --ip 192.168.123.164
+        """,
+    )
+    parser.add_argument("mode", choices=["record", "replay", "list", "live", "playlocal"],
+                        help="Operation mode")
+    parser.add_argument("--name", "-n", default="recording",
+                        help="Recording name (default: recording)")
+    parser.add_argument("--seconds", "-s", type=float, default=5.0,
+                        help="Recording duration in seconds (default: 5)")
+    parser.add_argument("--device", "-d", type=int, default=None,
+                        help="Audio input device index (default: auto-detect)")
+    parser.add_argument("--ip", default="192.168.123.164",
+                        help="G1 robot IP for replay/live (default: 192.168.123.164)")
+    parser.add_argument("--user", "-u", default="unitree",
+                        help="SSH user on G1 (default: unitree)")
+
+    args = parser.parse_args()
+
+    if args.mode == "list":
+        list_recordings()
+
+    elif args.mode == "record":
+        record_voice(args.name, args.seconds, args.device)
+
+    elif args.mode == "playlocal":
+        play_local(args.name)
+
+    elif args.mode == "replay":
+        replay_on_g1(args.name, args.ip, args.user)
+
+    elif args.mode == "live":
+        live_record_and_replay(args.seconds, args.ip, args.device, args.user)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/template_audio_script.py b/Audio_Recorder/template_audio_script.py
new file mode 100644
index 0000000..b0c9222
--- /dev/null
+++ b/Audio_Recorder/template_audio_script.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+TEMPLATE — G1 Audio Script
+
+Copy-paste starter demonstrating every reliable primitive for G1 audio:
+
+    1. Auto-detect and prep the Hollyland mic   (g1_audio_devices helpers)
+    2. Record N seconds via parec               (the method that actually works)
+    3. Load / generate PCM (16 kHz mono int16)
+    4. Play on the G1 speaker in ONE call       (_CallRequestWithParamAndBin)
+    5. Always STOP the stream at the end        (mandatory reset)
+
+Do NOT use:
+    - PlayStream (chunked — unreliable, see voice_note.txt)
+    - PyAudio input against the pulse device on the robot (gives silence)
+    - aplay / paplay to the built-in speaker (no physical path)
+
+Run on the robot, gemini conda env (needs _CallRequestWithParamAndBin):
+    /home/unitree/miniconda3/envs/gemini/bin/python3 template_audio_script.py
+
+Run on the workstation (edit MODE below to "tone" — no robot needed).
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import struct
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+# ─── Constants ──────────────────────────────────────────────────────────────
+TARGET_RATE = 16000
+BIT_DEPTH = 16
+CHANNELS = 1
+APP_NAME = "my_app"     # pick any unique label for YOUR script
+DDS_IFACE = "eth0"      # on workstation use "enp3s0"
+VOLUME = 100
+
+
+# ─── 1. Mic prep (optional — skip if recording is handled elsewhere) ────────
+def prep_mic(keywords=("hollyland", "wireless_microphone")):
+    """Auto-detect and unmute the first PulseAudio source matching keywords."""
+    rc = subprocess.run(["pactl", "list", "short", "sources"], capture_output=True, text=True)
+    for line in rc.stdout.splitlines():
+        parts = line.split("\t")
+        if len(parts) < 2:
+            continue
+        idx, name = parts[0], parts[1]
+        if any(k in name.lower() for k in keywords):
+            subprocess.run(["pactl", "set-default-source", name])
+            subprocess.run(["pactl", "set-source-mute", idx, "0"])
+            subprocess.run(["pactl", "set-source-volume", idx, "100%"])
+            return int(idx), name
+    return None, None
+
+
+# ─── 2. Record via parec (the method that works on the robot) ───────────────
+def record(seconds: float, source_index: int) -> bytes:
+    proc = subprocess.Popen(
+        ["parec", "-d", str(source_index),
+         "--format=s16le", "--rate=16000", "--channels=1", "--raw"],
+        stdout=subprocess.PIPE,
+    )
+    time.sleep(seconds)
+    proc.terminate()
+    return proc.stdout.read() if proc.stdout else b""
+
+
+# ─── 3. Generate a demo PCM (used when MODE=tone) ───────────────────────────
+def sine_tone(freq_hz: float, seconds: float) -> bytes:
+    n = int(TARGET_RATE * seconds)
+    out = bytearray()
+    for i in range(n):
+        val = int(0.3 * 32767 * math.sin(2 * math.pi * freq_hz * i / TARGET_RATE))
+        out += struct.pack("<h", val)
+    return bytes(out)
+
+
+# ─── 4. Play PCM on the G1 speaker in ONE call ──────────────────────────────
+def play_on_g1(pcm: bytes):
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+    from unitree_sdk2py.g1.audio.g1_audio_api import (
+        ROBOT_API_ID_AUDIO_START_PLAY, ROBOT_API_ID_AUDIO_STOP_PLAY,
+    )
+
+    ChannelFactoryInitialize(0, DDS_IFACE)
+    c = AudioClient()
+    c.SetTimeout(10.0)
+    c.Init()
+    c.SetVolume(VOLUME)
+
+    # 4a. Reset any previous stream first.
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+    time.sleep(0.3)
+
+    # 4b. Unique stream_id per play.
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": APP_NAME,
+        "stream_id": sid,
+        "sample_rate": TARGET_RATE,
+        "channels": CHANNELS,
+        "bits_per_sample": BIT_DEPTH,
+    })
+
+    # 4c. Send ALL audio in ONE call.
+    duration = len(pcm) / (TARGET_RATE * BIT_DEPTH // 8)
+    print(f"Playing {duration:.1f}s ...")
+    c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+    time.sleep(duration + 0.5)
+
+    # 4d. Stop = mandatory cleanup.
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP_NAME}))
+
+
+# ─── Main: pick a mode ──────────────────────────────────────────────────────
+def main() -> int:
+    p = argparse.ArgumentParser(description="G1 audio template")
+    p.add_argument("mode", choices=["tone", "echo", "prep-mic"],
+                   help="tone=440Hz beep; echo=record+play; prep-mic=auto-detect mic only")
+    p.add_argument("--seconds", "-s", type=float, default=3.0,
+                   help="Recording / tone duration (default 3)")
+    args = p.parse_args()
+
+    if args.mode == "prep-mic":
+        idx, name = prep_mic()
+        if idx is None:
+            print("No wireless mic found.")
+            return 1
+        print(f"Prepped: [{idx}] {name}")
+        return 0
+
+    if args.mode == "tone":
+        pcm = sine_tone(440.0, args.seconds)
+        play_on_g1(pcm)
+        return 0
+
+    if args.mode == "echo":
+        idx, name = prep_mic()
+        if idx is None:
+            print("No wireless mic found.")
+            return 1
+        print(f"Mic: [{idx}] {name}")
+        print(f"Recording {args.seconds:.1f}s ... speak now")
+        pcm = record(args.seconds, idx)
+        if not pcm:
+            print("No audio captured.")
+            return 2
+        print(f"Captured {len(pcm):,} bytes. Playing back on G1 ...")
+        play_on_g1(pcm)
+        return 0
+
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Audio_Recorder/test_g1_pcm.py b/Audio_Recorder/test_g1_pcm.py
new file mode 100644
index 0000000..24aabea
--- /dev/null
+++ b/Audio_Recorder/test_g1_pcm.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""Test different PCM streaming approaches on G1 voice service."""
+
+import sys
+import time
+import json
+import wave
+import numpy as np
+from pathlib import Path
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+from unitree_sdk2py.g1.audio.g1_audio_api import *
+
+if len(sys.argv) < 2:
+    print(f"Usage: python3 {sys.argv[0]} <network_interface>")
+    sys.exit(1)
+
+ChannelFactoryInitialize(0, sys.argv[1])
+client = AudioClient()
+client.SetTimeout(10.0)
+client.Init()
+
+# Load a small WAV chunk
+wav_path = Path(__file__).parent / "DataG1" / "greeting.wav"
+with wave.open(str(wav_path), "rb") as wf:
+    pcm_all = wf.readframes(wf.getnframes())
+    rate = wf.getframerate()
+    channels = wf.getnchannels()
+
+print(f"Loaded {len(pcm_all)} bytes, {rate}Hz, {channels}ch")
+chunk = pcm_all[:2048]  # small test chunk
+
+print("\n=== Approach 1: Register stream then send binary chunks ===")
+# Step 1: Open stream
+param = json.dumps({"app_name": "rec", "stream_id": "s1"})
+code, data = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param)
+print(f"  Open stream: code={code}, data={data}")
+
+# Step 2: Send binary chunk (no reply)
+try:
+    client._CallBinaryNoReply(ROBOT_API_ID_AUDIO_START_PLAY, list(chunk))
+    print("  _CallBinaryNoReply: sent (no reply)")
+except Exception as e:
+    print(f"  _CallBinaryNoReply: {e}")
+
+time.sleep(1)
+
+# Step 3: Stop
+param_stop = json.dumps({"app_name": "rec"})
+code, _ = client._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, param_stop)
+print(f"  Stop: code={code}")
+
+print("\n=== Approach 2: _CallNoReply with JSON+PCM ===")
+try:
+    param2 = json.dumps({"app_name": "rec2", "stream_id": "s2", "pcm": list(chunk[:512])})
+    client._CallNoReply(ROBOT_API_ID_AUDIO_START_PLAY, param2)
+    print("  _CallNoReply: sent")
+except Exception as e:
+    print(f"  _CallNoReply: {e}")
+
+time.sleep(1)
+
+print("\n=== Approach 3: Multiple small _CallBinary chunks ===")
+# Register first
+param3 = json.dumps({"app_name": "rec3", "stream_id": "s3"})
+code, _ = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param3)
+print(f"  Register: code={code}")
+
+for i in range(5):
+    off = i * 512
+    small = pcm_all[off:off+512]
+    try:
+        code, _ = client._CallBinary(ROBOT_API_ID_AUDIO_START_PLAY, list(small))
+        print(f"  Chunk {i}: code={code}")
+    except Exception as e:
+        print(f"  Chunk {i}: {e}")
+        break
+
+print("\n=== Approach 4: Check all registered API methods ===")
+for api_name, api_id in [
+    ("TTS", ROBOT_API_ID_AUDIO_TTS),
+    ("ASR", ROBOT_API_ID_AUDIO_ASR),
+    ("START_PLAY", ROBOT_API_ID_AUDIO_START_PLAY),
+    ("STOP_PLAY", ROBOT_API_ID_AUDIO_STOP_PLAY),
+    ("GET_VOLUME", ROBOT_API_ID_AUDIO_GET_VOLUME),
+    ("SET_VOLUME", ROBOT_API_ID_AUDIO_SET_VOLUME),
+    ("SET_RGB_LED", ROBOT_API_ID_AUDIO_SET_RGB_LED),
+]:
+    print(f"  {api_name} = {api_id}")
+
+print("\n=== Approach 5: TTS with file path (maybe voice service can play files?) ===")
+# Try passing a file path to TTS or a special API
+param5 = json.dumps({"file": "/tmp/greeting.wav"})
+code, data = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param5)
+print(f"  file path via START_PLAY: code={code}, data={data}")
+
+param5b = json.dumps({"url": "/tmp/greeting.wav"})
+code, data = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param5b)
+print(f"  url path via START_PLAY:  code={code}, data={data}")
+
+param5c = json.dumps({"path": "/tmp/greeting.wav"})
+code, data = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param5c)
+print(f"  path via START_PLAY:      code={code}, data={data}")
+
+print("\nDone. Did you hear anything?")
diff --git a/Audio_Recorder/test_g1_speaker.py b/Audio_Recorder/test_g1_speaker.py
new file mode 100644
index 0000000..f1dcb67
--- /dev/null
+++ b/Audio_Recorder/test_g1_speaker.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""Quick test: does the G1 built-in speaker work via AudioClient?"""
+
+import sys
+import time
+import json
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+from unitree_sdk2py.g1.audio.g1_audio_api import *
+
+if len(sys.argv) < 2:
+    print(f"Usage: python3 {sys.argv[0]} <network_interface>")
+    print(f"  e.g. python3 {sys.argv[0]} enp3s0")
+    sys.exit(1)
+
+iface = sys.argv[1]
+print(f"Connecting via {iface}...")
+ChannelFactoryInitialize(0, iface)
+
+client = AudioClient()
+client.SetTimeout(10.0)
+client.Init()
+
+# Test 1: Volume
+print("\n--- Test 1: GetVolume ---")
+code, vol = client.GetVolume()
+print(f"  code={code}, volume={vol}")
+
+print("\n--- Test 2: SetVolume(100) ---")
+code = client.SetVolume(100)
+print(f"  code={code}")
+
+# Test 3: LED (visual confirmation that RPC works)
+print("\n--- Test 3: LED Red ---")
+code = client.LedControl(255, 0, 0)
+print(f"  code={code}")
+time.sleep(1)
+
+print("--- Test 3b: LED Green ---")
+code = client.LedControl(0, 255, 0)
+print(f"  code={code}")
+time.sleep(1)
+
+print("--- Test 3c: LED off ---")
+code = client.LedControl(0, 0, 0)
+print(f"  code={code}")
+
+# Test 4: TTS
+print("\n--- Test 4: TTS 'Hello, testing speaker' ---")
+code = client.TtsMaker("Hello, testing speaker", 0)
+print(f"  code={code}")
+time.sleep(3)
+
+print("\n--- Test 5: TTS Chinese ---")
+code = client.TtsMaker("你好，测试声音", 0)
+print(f"  code={code}")
+time.sleep(3)
+
+# Test 6: Try _Call with AUDIO_START_PLAY different formats
+print("\n--- Test 6: _Call AUDIO_START_PLAY formats ---")
+import wave
+import numpy as np
+from pathlib import Path
+
+wav_path = Path(__file__).parent / "DataG1" / "greeting.wav"
+if wav_path.exists():
+    with wave.open(str(wav_path), "rb") as wf:
+        pcm = wf.readframes(wf.getnframes())
+    # Small chunk for testing
+    chunk = pcm[:4096]
+
+    # Format A: just JSON with pcm as list of ints
+    param_a = json.dumps({"pcm": list(chunk[:256])})
+    code_a, data_a = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param_a)
+    print(f"  Format A (_Call, pcm list):  code={code_a}")
+
+    # Format B: _CallBinary with raw bytes
+    code_b, data_b = client._CallBinary(ROBOT_API_ID_AUDIO_START_PLAY, list(chunk[:256]))
+    print(f"  Format B (_CallBinary, raw): code={code_b}")
+
+    # Format C: _Call with app_name
+    param_c = json.dumps({"app_name": "test", "stream_id": "s1"})
+    code_c, data_c = client._Call(ROBOT_API_ID_AUDIO_START_PLAY, param_c)
+    print(f"  Format C (_Call, app_name):  code={code_c}")
+else:
+    print(f"  No greeting.wav found, skipping PCM tests")
+
+print("\nDone. Did you hear anything or see LED changes?")
diff --git a/Audio_Recorder/test_marcus_audio.py b/Audio_Recorder/test_marcus_audio.py
new file mode 100644
index 0000000..fc0d4e4
--- /dev/null
+++ b/Audio_Recorder/test_marcus_audio.py
@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+"""
+Test script for Marcus env — TTS + Audio playback on G1 built-in speaker.
+Deploy to robot: scp test_marcus_audio.py unitree@192.168.123.164:/home/unitree/Marcus/
+Run on robot:    conda activate marcus && python3 ~/Marcus/test_marcus_audio.py
+"""
+
+import sys
+import time
+import json
+import struct
+import math
+
+def check_sdk():
+    """Check if marcus env has the required SDK methods."""
+    print("=== SDK CHECK ===")
+    try:
+        from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+        from unitree_sdk2py.rpc.client import Client
+        print("  unitree_sdk2py: installed")
+
+        has_bin = hasattr(Client, '_CallRequestWithParamAndBin')
+        print(f"  _CallRequestWithParamAndBin: {'YES' if has_bin else 'NO (need upgrade: pip install --upgrade unitree_sdk2py)'}")
+        return has_bin
+    except ImportError:
+        print("  unitree_sdk2py: NOT INSTALLED")
+        print("  Run: pip install unitree_sdk2py")
+        return False
+
+
+def test_tts():
+    """Test TTS on G1 speaker."""
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+
+    print("\n=== TTS TEST ===")
+    ChannelFactoryInitialize(0, "eth0")
+    c = AudioClient()
+    c.SetTimeout(10.0)
+    c.Init()
+
+    # Volume
+    code, vol = c.GetVolume()
+    print(f"  Volume: {vol}")
+    c.SetVolume(100)
+
+    # LED
+    print("  LED -> Green")
+    c.LedControl(0, 255, 0)
+    time.sleep(1)
+    c.LedControl(0, 0, 0)
+
+    # TTS English
+    print("  TTS: 'Hello, Marcus is ready'")
+    c.TtsMaker("Hello, Marcus is ready", 0)
+    time.sleep(4)
+
+    print("  TTS test done.")
+
+
+def test_audio_playback():
+    """Test audio playback with a sine tone."""
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+    from unitree_sdk2py.g1.audio.g1_audio_api import (
+        ROBOT_API_ID_AUDIO_START_PLAY,
+        ROBOT_API_ID_AUDIO_STOP_PLAY,
+    )
+
+    print("\n=== AUDIO PLAYBACK TEST ===")
+    ChannelFactoryInitialize(0, "eth0")
+    c = AudioClient()
+    c.SetTimeout(10.0)
+    c.Init()
+    c.SetVolume(100)
+
+    # Generate 2s sine tone at 440Hz
+    rate = 16000
+    duration = 2
+    pcm = b""
+    for i in range(rate * duration):
+        val = int(30000 * math.sin(2 * math.pi * 440 * i / rate))
+        pcm += struct.pack("<h", val)
+
+    print(f"  Sine tone: {len(pcm)} bytes, {rate}Hz, {duration}s")
+
+    # Stop previous, send all in one call with unique stream_id
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "test"}))
+    time.sleep(0.3)
+
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": "test",
+        "stream_id": sid,
+        "sample_rate": 16000,
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+
+    print("  Playing tone...")
+    result = c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+    print(f"  Result: {result}")
+    time.sleep(duration + 1)
+
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "test"}))
+    print("  Playback test done.")
+
+
+def main():
+    has_sdk = check_sdk()
+
+    if not has_sdk:
+        print("\nSDK missing or outdated. Fix with:")
+        print("  conda activate marcus")
+        print("  pip install --upgrade unitree_sdk2py")
+        sys.exit(1)
+
+    test_tts()
+    test_audio_playback()
+
+    print("\n=== ALL TESTS DONE ===")
+    print("Did you hear:")
+    print("  1. LED flash green?")
+    print("  2. 'Hello, Marcus is ready' (TTS)?")
+    print("  3. 2-second beep tone (audio playback)?")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Audio_Recorder/voice_note.txt b/Audio_Recorder/voice_note.txt
new file mode 100644
index 0000000..cadf991
--- /dev/null
+++ b/Audio_Recorder/voice_note.txt
@@ -0,0 +1,334 @@
+G1 Audio Playback & Recording — Working Configuration
+======================================================
+Date: 2026-04-07  (updated 2026-04-23)
+
+UPDATE 2026-04-23
+------------------
+Three scripts previously used the broken chunked-PlayStream path. Fixed:
+
+    g1_voice_recorder.py  —  replay path now sends ALL audio in ONE
+                             _CallRequestWithParamAndBin call with a unique
+                             stream_id. No more PlayStream.
+    g1_play_wav.py        —  same fix; removed TtsMaker warm-up (not needed).
+    g1_interactive_player.py  —  removed chunked loop (same stream_id across
+                             chunks was silently dropping after the first).
+
+Added three new scripts:
+
+    g1_audio_devices.py      —  pactl wrapper: list sources/sinks, find by
+                                keyword, auto-detect Hollyland mic, unmute,
+                                set volume, parec quick-test.
+    g1_edge_tts.py           —  edge-tts bilingual (AR+EN) TTS pipeline —
+                                text → MP3 → 16 kHz WAV → G1 speaker.
+                                Voices default to ar-AE-HamdanNeural / en-US-GuyNeural.
+    template_audio_script.py —  copy-paste starter demonstrating the
+                                reliable primitives (prep mic, record via
+                                parec, play in ONE call, always STOP).
+
+Quick reference — the ONE audio play path that works on G1:
+    from unitree_sdk2py.g1.audio.g1_audio_api import (
+        ROBOT_API_ID_AUDIO_START_PLAY, ROBOT_API_ID_AUDIO_STOP_PLAY)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP}))
+    time.sleep(0.3)
+    sid = f"s_{int(time.time()*1000)}"
+    param = json.dumps({"app_name": APP, "stream_id": sid,
+                        "sample_rate": 16000, "channels": 1, "bits_per_sample": 16})
+    c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+    time.sleep(duration + 0.5)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": APP}))
+
+------------------
+
+
+
+BUILT-IN SPEAKER PLAYBACK (via Unitree Voice Service)
+-----------------------------------------------------
+Method: _CallRequestWithParamAndBin (NOT PlayStream)
+Key discovery: JSON params MUST include sample_rate, channels, bits_per_sample
+Required: Send ALL audio in ONE call (streaming chunks does NOT work)
+Required: Unique stream_id per play (timestamp-based), or audio won't repeat
+Required: Call AUDIO_STOP_PLAY before each new play to reset stream
+
+Working code pattern:
+    from unitree_sdk2py.g1.audio.g1_audio_api import *
+
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "p"}))
+    time.sleep(0.3)
+
+    sid = f"s_{int(time.time() * 1000)}"
+    param = json.dumps({
+        "app_name": "p",
+        "stream_id": sid,           # MUST be unique each time
+        "sample_rate": 16000,       # MUST include these format params
+        "channels": 1,
+        "bits_per_sample": 16,
+    })
+    c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+    time.sleep(duration + 1)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({"app_name": "p"}))
+
+Audio format: 16kHz, mono, 16-bit signed little-endian PCM
+SDK required: Robot's gemini conda env (has _CallRequestWithParamAndBin)
+Workstation SDK (g1_env): Does NOT have _CallRequestWithParamAndBin — must run on robot
+
+What does NOT work:
+    - PlayStream() method (sends data but no audio output)
+    - Streaming chunks in a loop (only first call sometimes plays)
+    - _CallBinary / _Call with PCM data (error code 100 from workstation SDK)
+    - Running playback from .py script files (only python3 -c inline works reliably)
+    - aplay / paplay through PulseAudio (Jetson platform-sound has no physical speaker)
+
+TTS (Text-to-Speech):
+    - English: works
+    - Chinese: works
+    - Arabic: does NOT work (falls back to Chinese)
+    - speaker_id: 0, 1, 2 all work (different voices)
+
+API IDs:
+    ROBOT_API_ID_AUDIO_TTS         = 1001
+    ROBOT_API_ID_AUDIO_ASR         = 1002  (returns 3104 — not available)
+    ROBOT_API_ID_AUDIO_START_PLAY  = 1003
+    ROBOT_API_ID_AUDIO_STOP_PLAY   = 1004
+    ROBOT_API_ID_AUDIO_GET_VOLUME  = 1005
+    ROBOT_API_ID_AUDIO_SET_VOLUME  = 1006
+    ROBOT_API_ID_AUDIO_SET_RGB_LED = 1010
+    APIs 1007-1009, 1011-1012: not registered (code 3103)
+
+WIRELESS MICROPHONE RECORDING
+------------------------------
+Device: Hollyland Wireless Microphone (USB)
+    Vendor: Shenzhen Hollyland Technology Co.,Ltd
+    Product ID: 0007
+    ALSA: card 2, device 0 (hw:2,0)
+    PulseAudio source index: 3
+    Source name: alsa_input.usb-Shenzhen_Hollyland_Technology_Co._Ltd_Wireless_microphone_C63X223T6MX-01.analog-stereo
+    Format: s24le, 2ch, 48000Hz
+
+CRITICAL: Mic was MUTED by default. Must unmute before recording.
+
+Step-by-step mic setup commands (run on robot):
+    # 1. View all PulseAudio sources (find mic index)
+    pactl list sources short
+
+    # 2. Check mic details (mute status, volume, format)
+    pactl list sources | grep -A 10 "Wireless"
+
+    # 3. Set wireless mic as default input source
+    pactl set-default-source alsa_input.usb-Shenzhen_Hollyland_Technology_Co._Ltd_Wireless_microphone_C63X223T6MX-01.analog-stereo
+
+    # 4. Unmute the mic (index 3)
+    pactl set-source-mute 3 0
+
+    # 5. Set mic volume to 100%
+    pactl set-source-volume 3 100%
+
+    # 6. Verify mic is capturing audio
+    timeout 2 parec -d 3 --format=s16le --rate=16000 --channels=1 --raw > /tmp/raw_test.pcm
+    python3 -c "
+    import numpy as np
+    a = np.fromfile('/tmp/raw_test.pcm', dtype=np.int16)
+    print(f'Samples={len(a)}, min={a.min()}, max={a.max()}, std={a.std():.0f}')
+    if a.std() > 50: print('MIC WORKS!')
+    else: print('Still silent - check transmitter is ON')
+    "
+
+Recording method (parec, not PyAudio — PyAudio gives silence through pulse device):
+    subprocess.Popen(['parec', '-d', '3', '--format=s16le', '--rate=16000', '--channels=1', '--raw'], stdout=subprocess.PIPE)
+    time.sleep(duration)
+    proc.terminate()
+    raw = proc.stdout.read()
+
+PyAudio recording through device index 25 (pulse) gives ALL ZEROS even after unmuting.
+Must use parec with source index 3 directly.
+
+FULL RECORD + PLAYBACK EXAMPLE (run on robot in gemini conda env):
+    python3 -c "
+    import time, wave, json, numpy as np, subprocess
+    from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+    from unitree_sdk2py.g1.audio.g1_audio_client import AudioClient
+    from unitree_sdk2py.g1.audio.g1_audio_api import *
+
+    # Record 5 seconds via parec (wireless mic, source index 3)
+    print('Recording 5 seconds... speak now!')
+    proc = subprocess.Popen(
+        ['parec', '-d', '3', '--format=s16le', '--rate=16000', '--channels=1', '--raw'],
+        stdout=subprocess.PIPE)
+    time.sleep(5)
+    proc.terminate()
+    raw = proc.stdout.read()
+    audio = np.frombuffer(raw, dtype=np.int16)
+    print(f'Recorded {len(audio)} samples, std={audio.std():.0f}')
+
+    # Save to WAV
+    path = '/home/unitree/SanadVoice/recorded voices/my_recording.wav'
+    wf = wave.open(path, 'wb')
+    wf.setnchannels(1)
+    wf.setsampwidth(2)
+    wf.setframerate(16000)
+    wf.writeframes(audio.tobytes())
+    wf.close()
+    print(f'Saved: {path}')
+
+    # Play back on G1 built-in speaker
+    ChannelFactoryInitialize(0, 'eth0')
+    c = AudioClient()
+    c.SetTimeout(10.0)
+    c.Init()
+    c.SetVolume(100)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({'app_name':'p'}))
+    time.sleep(0.3)
+
+    pcm = audio.tobytes()
+    sid = f's_{int(time.time()*1000)}'
+    param = json.dumps({
+        'app_name': 'p',
+        'stream_id': sid,
+        'sample_rate': 16000,
+        'channels': 1,
+        'bits_per_sample': 16
+    })
+    c._CallRequestWithParamAndBin(ROBOT_API_ID_AUDIO_START_PLAY, param, list(pcm))
+    time.sleep(len(audio)/16000 + 1)
+    c._Call(ROBOT_API_ID_AUDIO_STOP_PLAY, json.dumps({'app_name':'p'}))
+    print('Done')
+    "
+
+NETWORK & SSH
+--------------
+Robot IP: 192.168.123.164
+SSH user: unitree
+SSH pass: 123
+SSH key: configured (ssh-copy-id done)
+DDS interface on robot: eth0
+DDS interface on workstation: enp3s0
+
+Robot conda envs:
+    - base: no unitree_sdk2py
+    - gemini: has unitree_sdk2py with PlayStream + _CallRequestWithParamAndBin
+    Python: /home/unitree/miniconda3/envs/gemini/bin/python3
+
+RUNNING SERVICES ON ROBOT
+---------------------------
+PulseAudio: PID 1752 (holds hw:1,0 speaker and hw:2,0 mic)
+sanad_webserver.py: PID 1815 (gemini_voice_v2)
+sanad_voice.py: PID ~4640+ (gemini_voice)
+Systemd: sanad_voice.service (auto-start)
+
+PulseAudio sink (speaker): alsa_output.platform-sound.analog-stereo (card 1 APE device 0)
+    - Volume: 100%, not muted
+    - Does NOT produce audible output (no physical speaker on this path)
+    - Built-in speaker is only accessible through Unitree voice RPC service
+
+SanadVoice project speaker: Anker PowerConf USB (external, not always connected)
+    Sink: alsa_output.usb-Anker_PowerConf_A3321-DEV-SN1-01.analog-stereo
+    Source: alsa_input.usb-Anker_PowerConf_A3321-DEV-SN1-01.mono-fallback
+
+RECORDED VOICES LOCATION
+--------------------------
+Robot: /home/unitree/SanadVoice/recorded voices/  (54 WAV files, English)
+Robot: /home/unitree/SanadVoice/Audio/             (additional audio)
+Workstation: ~/Robotics_workspace/yslootahtech/G1_Lootah/Audio_Recorder/DataG1/
+
+MARCUS TTS CONFIGURATION (2026-04-08)
+======================================
+Backend: edge-tts (online, Microsoft Edge TTS API, no API key needed)
+Requires: Internet connection on robot
+
+Voices:
+    Arabic:  ar-AE-HamdanNeural  (UAE male)
+    English: en-US-GuyNeural     (US male)
+    Note: For same voice both languages, use ar-AE-HamdanNeural for both
+          (English will have slight Arabic accent)
+
+Config: /home/unitree/Marcus/Config/config_Voice.json
+    "en_backend": "edge_tts"
+    "ar_backend": "edge_tts"
+    "edge_voice_ar": "ar-AE-HamdanNeural"
+    "edge_voice_en": "en-US-GuyNeural"
+
+Pipeline:
+    audio_api.speak(text, lang)
+        → edge-tts generates MP3 (1-2s)
+        → pydub converts MP3 → 16kHz WAV
+        → _CallRequestWithParamAndBin → G1 speaker
+        → mic muted during playback, unmuted after
+
+Fallback: Built-in TtsMaker for English if edge-tts fails (no internet)
+
+Mic: Auto-detected by scanning PulseAudio for "wireless"/"hollyland"/"usb"
+    Auto-unmutes, sets volume 100%, sets as default source
+    Uses parec (not PyAudio) for recording
+    Muted during TTS playback to prevent self-listening
+
+Files updated:
+    /home/unitree/Marcus/API/audio_api.py     — XTTS + edge-tts + builtin backends
+    /home/unitree/Marcus/Voice/marcus_voice.py — Uses audio_api's auto-detected mic
+    /home/unitree/Marcus/Voice/xtts_server.py  — XTTS-v2 persistent server (for offline use later)
+    /home/unitree/Marcus/Config/config_Voice.json — TTS config
+
+Conda envs:
+    marcus     (Python 3.8) — Brain, vision, controller, audio playback SDK
+    marcus_tts (Python 3.10) — XTTS-v2, SILMA TTS (for future offline use)
+    gemini     (Python 3.11) — Has _CallRequestWithParamAndBin
+
+TTS OPTIONS TESTED
+-------------------
+| Backend          | Arabic | English | Offline | Speed  | Status        |
+|------------------|--------|---------|---------|--------|---------------|
+| edge-tts         | Yes    | Yes     | No      | 1-2s   | WORKING ✓     |
+| Built-in TtsMaker| No     | Yes     | Yes     | 0s     | WORKING ✓     |
+| XTTS-v2 (CPU)   | Yes    | Yes     | Yes     | ~14s   | WORKING (slow)|
+| XTTS-v2 (GPU)   | Yes    | Yes     | Yes     | ~2s    | NO (CUDA 11.4 no cp310 wheel) |
+| SILMA TTS        | Yes    | Yes     | Yes     | ?      | BROKEN (deps) |
+| Piper TTS        | Yes    | No      | Yes     | ~1s    | BROKEN (piper_phonemize on ARM64) |
+| gTTS             | Yes    | Yes     | No      | 1-2s   | WORKING (female only) |
+| SpeechT5 local   | Yes    | No      | Yes     | ~2-3s  | NOT TESTED (model on robot) |
+
+JETSON ORIN NX LIMITATIONS
+---------------------------
+- JetPack 5.1.1, L4T R35.3.1, CUDA 11.4
+- NVIDIA only provides PyTorch cp38 wheels (Python 3.8)
+- No cp310 GPU PyTorch available — must build from source
+- scikit-learn TLS bug on aarch64 (libgomp cannot allocate memory in static TLS block)
+  Fix: LD_PRELOAD=$(find env/lib -name "libgomp*.so*" | head -1)
+- apt is broken (unmet dependencies), cannot install espeak-ng
+
+SCRIPTS DEPLOYED ON ROBOT
+---------------------------
+/home/unitree/g1_interactive_player.py  — Interactive WAV selector + playback
+/home/unitree/g1_play_wav.py            — CLI WAV player
+/home/unitree/g1_tts_test.py            — TTS language test
+/home/unitree/Marcus/Voice/xtts_server.py — XTTS-v2 persistent server
+
+FILE INVENTORY (workstation /G1_Lootah/Audio_Recorder)
+-------------------------------------------------------
+Recording + replay:
+    g1_voice_recorder.py        — workstation PyAudio record → scp → G1 replay
+    g1_play_wav.py              — (on-robot) play any WAV on G1 speaker
+    g1_interactive_player.py    — (on-robot) interactive WAV picker
+    g1_voice_deploy.py          — deploy recordings to robot
+Device management:
+    g1_audio_devices.py         — pactl wrapper: list/find/auto-hollyland/quick-test
+TTS:
+    g1_edge_tts.py              — Microsoft Edge TTS bilingual (AR+EN)
+    g1_tts_arabic.py            — Piper / tts_arabic offline TTS (ARM64 broken per above)
+    g1_tts_test.py              — TTS smoke test (English only)
+Templates + tests:
+    template_audio_script.py    — copy-paste starter with the reliable primitives
+    test_g1_speaker.py          — speaker init sanity check
+    test_g1_pcm.py              — raw PCM playback test
+    test_marcus_audio.py        — Marcus env audio pipeline end-to-end
+Data:
+    DataG1/                     — local recordings (WAV, 16 kHz mono int16)
+
+Run recipes:
+    # Workstation side
+    python3 g1_voice_recorder.py record --name hello --seconds 5
+    python3 g1_voice_recorder.py replay --name hello --ip 192.168.123.164
+    python3 g1_edge_tts.py "Good morning" --lang en --save out.wav --no-play
+
+    # On robot (gemini env)
+    python3 g1_audio_devices.py auto-hollyland
+    python3 g1_play_wav.py /path/to/file.wav
+    python3 g1_edge_tts.py "مرحبا بكم" --lang ar
+    python3 template_audio_script.py echo --seconds 3
diff --git a/Camera/CameraFunc/laptop_viewer.py b/Camera/CameraFunc/laptop_viewer.py
new file mode 100644
index 0000000..a9863c4
--- /dev/null
+++ b/Camera/CameraFunc/laptop_viewer.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+"""
+Laptop ZMQ JPEG viewer (OpenCV window)
+- Subscribes to robot ZMQ PUB stream (single-part JPEG)
+- Displays live video in a popup window
+- Optional FPS + frame age overlay
+
+Run:
+  pip install pyzmq opencv-python numpy
+  python laptop_zmq_viewer.py --robot 192.168.123.164 --port 55555
+
+Keys:
+  q  -> quit
+  f  -> toggle overlay
+"""
+
+import time
+import argparse
+import zmq
+import cv2
+import numpy as np
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--robot", default="10.255.254.86", help="Robot IP that publishes ZMQ JPEG")
+    ap.add_argument("--port", type=int, default=55555, help="ZMQ port (from cam_config_client.yaml)")
+    ap.add_argument("--hwm", type=int, default=1, help="ZMQ high-water-mark (keep latest frames)")
+    ap.add_argument("--timeout_ms", type=int, default=1000, help="ZMQ poll timeout")
+    ap.add_argument("--window", default="G1 Head Camera", help="OpenCV window name")
+    args = ap.parse_args()
+
+    robot_ip = args.robot
+    port = args.port
+
+    # -----------------------
+    # ZMQ subscriber
+    # -----------------------
+    ctx = zmq.Context.instance()
+    sub = ctx.socket(zmq.SUB)
+    sub.setsockopt(zmq.RCVHWM, args.hwm)
+    sub.setsockopt(zmq.LINGER, 0)
+    sub.setsockopt_string(zmq.SUBSCRIBE, "")
+    sub.connect(f"tcp://{robot_ip}:{port}")
+
+    poller = zmq.Poller()
+    poller.register(sub, zmq.POLLIN)
+
+    print(f"[INFO] Subscribing to tcp://{robot_ip}:{port}")
+    print("[INFO] Press 'q' to quit, 'f' to toggle overlay")
+
+    # -----------------------
+    # Stats
+    # -----------------------
+    show_overlay = True
+    last_frame_ts = 0.0
+    fps = 0.0
+    fps_count = 0
+    t0 = time.monotonic()
+
+    cv2.namedWindow(args.window, cv2.WINDOW_NORMAL)
+
+    while True:
+        events = dict(poller.poll(timeout=args.timeout_ms))
+        if sub not in events:
+            # no frame received
+            # show a black frame with warning (optional)
+            black = np.zeros((480, 640, 3), dtype=np.uint8)
+            cv2.putText(black, "No frames...", (20, 50),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2, cv2.LINE_AA)
+            cv2.imshow(args.window, black)
+        else:
+            jpg = sub.recv()  # single-part JPEG bytes
+            arr = np.frombuffer(jpg, dtype=np.uint8)
+            img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+
+            if img is None:
+                continue
+
+            now = time.monotonic()
+            last_frame_ts = now
+
+            # FPS estimate (every 10 frames)
+            fps_count += 1
+            if fps_count >= 10:
+                dt = now - t0
+                if dt > 0:
+                    fps = fps_count / dt
+                t0 = now
+                fps_count = 0
+
+            if show_overlay:
+                age_ms = (time.monotonic() - last_frame_ts) * 1000.0
+                text = f"FPS: {fps:.1f} | Age: {age_ms:.0f} ms"
+                cv2.putText(img, text, (12, 28),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
+
+            cv2.imshow(args.window, img)
+
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            break
+        if key == ord("f"):
+            show_overlay = not show_overlay
+
+    cv2.destroyAllWindows()
+    sub.close()
+    ctx.term()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Camera/G1CAM/web_viewer.py b/Camera/G1CAM/web_viewer.py
new file mode 100644
index 0000000..9c14041
--- /dev/null
+++ b/Camera/G1CAM/web_viewer.py
@@ -0,0 +1,239 @@
+#!/usr/bin/env python3
+"""
+ZMQ (JPEG) -> Flask MJPEG Web Viewer
+- Subscribes to a ZMQ PUB stream (single-part JPEG frames)
+- Serves a browser page + MJPEG endpoint
+
+Usage:
+  On the robot:
+   conda activate teleimager
+   cd teleimager
+   python -m teleimager.image_server --rs
+
+  On the  PC:
+   python web_server.py
+   # then open:
+   http://<THIS_MACHINE_IP>:8080
+ 
+Tip:
+- If you run this ON the robot, set ROBOT_IP="127.0.0.1"
+- If you run this on your laptop, set ROBOT_IP to the robot IP (e.g. 192.168.123.164)
+"""
+
+import os
+import time
+import signal
+import threading
+from typing import Optional, Tuple
+
+import zmq
+import cv2
+import numpy as np
+from flask import Flask, Response, request
+
+# -----------------------
+# Config (env override)
+# -----------------------
+ROBOT_IP = os.getenv("ROBOT_IP", "10.255.254.86")   # set "127.0.0.1" if running on robot
+ZMQ_PORT = int(os.getenv("ZMQ_PORT", "55555"))
+HTTP_HOST = os.getenv("HTTP_HOST", "0.0.0.0")
+HTTP_PORT = int(os.getenv("HTTP_PORT", "8080"))
+JPEG_QUALITY = int(os.getenv("JPEG_QUALITY", "80"))
+FRAME_TIMEOUT_MS = int(os.getenv("FRAME_TIMEOUT_MS", "1000"))  # poll timeout for ZMQ
+SHOW_FPS = os.getenv("SHOW_FPS", "1") == "1"
+
+app = Flask(__name__)
+
+# -----------------------
+# ZMQ Subscriber Worker
+# -----------------------
+class LatestFrameBuffer:
+    """Thread-safe buffer storing only the latest JPEG bytes received."""
+    def __init__(self):
+        self._lock = threading.Lock()
+        self._jpg: Optional[bytes] = None
+        self._last_ts = 0.0
+        self._fps = 0.0
+        self._count = 0
+        self._t0 = None
+
+    def update(self, jpg: bytes) -> None:
+        now = time.monotonic()
+        with self._lock:
+            self._jpg = jpg
+            self._last_ts = now
+
+            # FPS calc
+            if self._t0 is None:
+                self._t0 = now
+            self._count += 1
+            if self._count >= 10:
+                dt = now - self._t0
+                if dt > 0:
+                    self._fps = self._count / dt
+                self._t0 = now
+                self._count = 0
+
+    def get(self) -> Tuple[Optional[bytes], float, float]:
+        with self._lock:
+            return self._jpg, self._fps, self._last_ts
+
+
+class ZMQCameraSubscriber(threading.Thread):
+    """Background thread that subscribes to ZMQ and stores the latest frame."""
+    def __init__(self, host: str, port: int, buffer: LatestFrameBuffer):
+        super().__init__(daemon=True)
+        self.host = host
+        self.port = port
+        self.buffer = buffer
+        self._stop = threading.Event()
+
+        self.ctx = zmq.Context.instance()
+        self.sub = self.ctx.socket(zmq.SUB)
+        self.sub.setsockopt(zmq.RCVHWM, 1)     # keep only latest
+        self.sub.setsockopt(zmq.LINGER, 0)
+        self.sub.setsockopt_string(zmq.SUBSCRIBE, "")
+        self.sub.connect(f"tcp://{self.host}:{self.port}")
+
+        self.poller = zmq.Poller()
+        self.poller.register(self.sub, zmq.POLLIN)
+
+    def run(self):
+        while not self._stop.is_set():
+            try:
+                events = dict(self.poller.poll(timeout=FRAME_TIMEOUT_MS))
+                if self.sub in events:
+                    msg = self.sub.recv()  # single-part JPEG
+                    if msg:
+                        self.buffer.update(msg)
+                else:
+                    # no frame received in timeout
+                    continue
+            except Exception:
+                time.sleep(0.05)
+
+        try:
+            self.sub.close()
+        except Exception:
+            pass
+
+    def stop(self):
+        self._stop.set()
+
+
+buffer = LatestFrameBuffer()
+subscriber = ZMQCameraSubscriber(ROBOT_IP, ZMQ_PORT, buffer)
+subscriber.start()
+
+# -----------------------
+# Helpers
+# -----------------------
+def decode_jpeg(jpg: bytes) -> Optional[np.ndarray]:
+    try:
+        arr = np.frombuffer(jpg, dtype=np.uint8)
+        img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+        return img
+    except Exception:
+        return None
+
+def overlay_status(img: np.ndarray, fps: float, last_ts: float) -> np.ndarray:
+    if img is None:
+        return img
+    now = time.monotonic()
+    age_ms = (now - last_ts) * 1000.0 if last_ts > 0 else 0.0
+    text = f"FPS: {fps:.1f}   Age: {age_ms:.0f} ms"
+    cv2.putText(img, text, (12, 28), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)
+    return img
+
+# -----------------------
+# MJPEG streaming
+# -----------------------
+def mjpeg_generator():
+    while True:
+        jpg, fps, last_ts = buffer.get()
+        if jpg is None:
+            time.sleep(0.02)
+            continue
+
+        # Optional: decode->overlay->re-encode (adds CPU)
+        if SHOW_FPS:
+            img = decode_jpeg(jpg)
+            if img is None:
+                time.sleep(0.01)
+                continue
+            img = overlay_status(img, fps, last_ts)
+            ok, out = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY])
+            if not ok:
+                continue
+            payload = out.tobytes()
+        else:
+            payload = jpg  # send original JPEG directly (fastest)
+
+        yield (
+            b"--frame\r\n"
+            b"Content-Type: image/jpeg\r\n"
+            b"Cache-Control: no-cache\r\n\r\n" + payload + b"\r\n"
+        )
+
+# -----------------------
+# Routes
+# -----------------------
+@app.route("/")
+def index():
+    # Allow manual override: /?w=1280
+    w = request.args.get("w", "1280")
+    return f"""
+    <html>
+    <head>
+      <meta name="viewport" content="width=device-width, initial-scale=1.0"/>
+      <title>Robot Head Camera</title>
+    </head>
+    <body style="margin:0;background:#111;">
+      <div style="padding:10px;color:#fff;font-family:Arial;">
+        <b>Head Camera</b> | ZMQ: {ROBOT_IP}:{ZMQ_PORT} | MJPEG: {HTTP_HOST}:{HTTP_PORT}
+      </div>
+      <div style="display:flex;justify-content:center;">
+        <img src="/video" style="width:100%;max-width:{w}px;height:auto;border:0;"/>
+      </div>
+    </body>
+    </html>
+    """
+
+@app.route("/video")
+def video():
+    return Response(
+        mjpeg_generator(),
+        mimetype="multipart/x-mixed-replace; boundary=frame"
+    )
+
+@app.route("/health")
+def health():
+    jpg, fps, last_ts = buffer.get()
+    ok = jpg is not None
+    age = (time.monotonic() - last_ts) if last_ts else None
+    return {
+        "ok": ok,
+        "robot_ip": ROBOT_IP,
+        "zmq_port": ZMQ_PORT,
+        "fps": fps,
+        "age_sec": age,
+    }
+
+# -----------------------
+# Clean shutdown
+# -----------------------
+def _shutdown(*_):
+    try:
+        subscriber.stop()
+    except Exception:
+        pass
+    time.sleep(0.1)
+    raise SystemExit(0)
+
+signal.signal(signal.SIGINT, _shutdown)
+signal.signal(signal.SIGTERM, _shutdown)
+
+if __name__ == "__main__":
+    print(f"[INFO] Subscribing to: tcp://{ROBOT_IP}:{ZMQ_PORT}")
+    print(f"[INFO] Open in browser: http://<this_machine_ip>:{HTTP_PORT}")
+    app.run(host=HTTP_HOST, port=HTTP_PORT, threaded=True)
diff --git a/Camera_Recorder/DataG1/arm_home.jsonl b/Camera_Recorder/DataG1/arm_home.jsonl
new file mode 100644
index 0000000..a7b1b62
--- /dev/null
+++ b/Camera_Recorder/DataG1/arm_home.jsonl
@@ -0,0 +1,482 @@
+{"meta": {"hz": 60.0, "motors": 29}}
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diff --git a/Camera_Recorder/DataG1/bird.jsonl b/Camera_Recorder/DataG1/bird.jsonl
new file mode 100644
index 0000000..97a01c9
--- /dev/null
+++ b/Camera_Recorder/DataG1/bird.jsonl
@@ -0,0 +1,1051 @@
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diff --git a/Camera_Recorder/DataG1/change_battery.jsonl b/Camera_Recorder/DataG1/change_battery.jsonl
new file mode 100644
index 0000000..5f95c92
--- /dev/null
+++ b/Camera_Recorder/DataG1/change_battery.jsonl
@@ -0,0 +1,1791 @@
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diff --git a/Camera_Recorder/DataG1/test2(1).jsonl b/Camera_Recorder/DataG1/test2(1).jsonl
new file mode 100644
index 0000000..d4739c9
--- /dev/null
+++ b/Camera_Recorder/DataG1/test2(1).jsonl
@@ -0,0 +1,665 @@
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diff --git a/Camera_Recorder/DataG1/test2.jsonl b/Camera_Recorder/DataG1/test2.jsonl
new file mode 100644
index 0000000..34935c4
--- /dev/null
+++ b/Camera_Recorder/DataG1/test2.jsonl
@@ -0,0 +1,1019 @@
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diff --git a/Camera_Recorder/Models/yolo11n-pose.pt b/Camera_Recorder/Models/yolo11n-pose.pt
new file mode 100644
index 0000000..f8e7370
Binary files /dev/null and b/Camera_Recorder/Models/yolo11n-pose.pt differ
diff --git a/Camera_Recorder/README.md b/Camera_Recorder/README.md
new file mode 100644
index 0000000..79862fa
--- /dev/null
+++ b/Camera_Recorder/README.md
@@ -0,0 +1,118 @@
+# Camera Recorder
+
+This recorder uses YOLO pose tracking from a camera or video source, then writes:
+
+- `DataG1/<take>.jsonl`: replay-compatible G1 dataset with 29 joints
+- `RawPose/<take>.pose.jsonl`: raw 2D pose keypoints and diagnostics
+- `Models/yolo11n-pose.pt`: local bundled YOLO pose model
+- `camera_recorder_config.json`: recorder defaults, runtime settings, and file locations
+- `joint.json`: robot joint counts, waist indices, and joint limits
+- `pose_mapping.json`: camera pose-to-joint mapping rules and gains
+
+The generated G1 dataset is designed to work with the existing replay scripts in this project. Lower body joints stay at `arm_home.jsonl`, and the upper body is estimated from the visible 2D pose.
+
+## Record From Camera
+
+```bash
+cd /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder
+python3 g1_camera_pose_recorder.py --source ask --output hands_up_cam
+```
+
+Recorder defaults also come from `camera_recorder_config.json`. Right now the default output is `test_take`, and the default home pose is the local `DataG1/arm_home.jsonl`, so you can simply run:
+
+```bash
+cd /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder
+python3 g1_camera_pose_recorder.py
+```
+
+The recorder asks for an `Enter` confirmation before it starts writing files. Use `--no-prompt` to skip that.
+
+You can also use explicit aliases like `--output-file` and `--home-file`.
+
+By default, the recorder now looks for `Models/yolo11n-pose.pt` first.
+
+All operational defaults are now loaded from `camera_recorder_config.json`. CLI flags still work, but they override the JSON values for that run only. The mapper-specific data is split into `joint.json` and `pose_mapping.json`, which are referenced from `camera_recorder_config.json`.
+
+Use a different config file:
+
+```bash
+python3 g1_camera_pose_recorder.py --config /path/to/camera_recorder_config.json --source 0 --output test_take
+```
+
+Fixed camera index:
+
+```bash
+python3 g1_camera_pose_recorder.py --source 0 --output hands_up_cam
+```
+
+Record for a fixed duration:
+
+```bash
+python3 g1_camera_pose_recorder.py --source 0 --output hands_up_cam --seconds 12
+```
+
+Skip the start confirmation prompt:
+
+```bash
+python3 g1_camera_pose_recorder.py --no-prompt
+```
+
+Use a custom YOLO model or custom home pose:
+
+```bash
+python3 g1_camera_pose_recorder.py \
+  --source 0 \
+  --output wave_cam \
+  --model /home/zedx/Robotics_workspace/AI/YOLO/yolo11n-pose.pt \
+  --home-pose /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Manual_Recorder/DataG1/arm_home.jsonl
+```
+
+## Replay The Recorded Dataset
+
+Because the file is saved under `Camera_Recorder/DataG1`, pass the full path to replay:
+
+```bash
+python3 /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/g1_camera_pose_replay.py \
+  enp3s0 \
+  --input /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/DataG1/hands_up_cam.jsonl \
+  --home arm_home.jsonl
+```
+
+`g1_camera_pose_replay.py` also reads replay defaults from `camera_recorder_config.json`, so from inside `Camera_Recorder` you can run:
+
+```bash
+python3 g1_camera_pose_replay.py
+```
+
+Replay scripts also accept explicit aliases like `--iface`, `--input-file`, and `--home-file`.
+
+Safer camera-aware replay:
+
+```bash
+python3 /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/g1_camera_pose_replay_v2.py \
+  enp3s0 \
+  --input /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/DataG1/hands_up_cam.jsonl \
+  --home arm_home.jsonl \
+  --speed 0.35
+```
+
+`g1_camera_pose_replay_v2.py` also reads replay defaults from `camera_recorder_config.json`. Right now those defaults are:
+
+- `iface = enp3s0`
+- `input = test_take.jsonl`
+- `home = arm_home.jsonl`
+
+So you can run:
+
+```bash
+cd /home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder
+python3 g1_camera_pose_replay_v2.py
+```
+
+## Notes
+
+- `q` or `Esc` stops recording.
+- `Ctrl+C` cleanly cancels camera selection or the start prompt, and stops recording if it is already running.
+- Any joint that is not captured in a frame falls back to the `arm_home.jsonl` pose for that joint.
+- The mapping is heuristic because a single 2D camera cannot recover full 3D shoulder and wrist orientation.
+- On this machine, the default `python3` does not currently have `ultralytics`; run the recorder in the same Python environment you already use for `/home/zedx/Robotics_workspace/AI/YOLO/YOLO_Test_3.py`.
diff --git a/Camera_Recorder/RawPose/test2(1).pose.jsonl b/Camera_Recorder/RawPose/test2(1).pose.jsonl
new file mode 100644
index 0000000..829361d
--- /dev/null
+++ b/Camera_Recorder/RawPose/test2(1).pose.jsonl
@@ -0,0 +1,665 @@
+{"meta": {"format": "g1_camera_pose_raw_v1", "created_unix": 1773399405.583534, "source": "0", "model": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/Models/yolo11n-pose.pt", "kpt_conf": 0.35, "dataset_file": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/DataG1/test2(1).jsonl", "config_file": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/camera_recorder_config.json", "joint_config_file": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/joint.json", "pose_mapping_file": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Camera_Recorder/pose_mapping.json"}}
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diff --git a/Camera_Recorder/RawPose/test2.pose.jsonl b/Camera_Recorder/RawPose/test2.pose.jsonl
new file mode 100644
index 0000000..5850774
--- /dev/null
+++ b/Camera_Recorder/RawPose/test2.pose.jsonl
@@ -0,0 +1,1019 @@
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diff --git a/Camera_Recorder/camera_recorder_config.json b/Camera_Recorder/camera_recorder_config.json
new file mode 100644
index 0000000..4bbe0c3
--- /dev/null
+++ b/Camera_Recorder/camera_recorder_config.json
@@ -0,0 +1,96 @@
+{
+  "paths": {
+    "data_dir": "DataG1",
+    "raw_pose_dir": "RawPose",
+    "default_home_pose": "DataG1/arm_home.jsonl",
+    "joint_config": "joint.json",
+    "pose_mapping_config": "pose_mapping.json",
+    "default_model_candidates": [
+      "Models/yolo11n-pose.pt",
+      "yolo11n-pose.pt",
+      "/home/zedx/Robotics_workspace/AI/YOLO/yolo11n-pose.pt"
+    ],
+    "model_fallback": "yolo11n-pose.pt"
+  },
+  "runtime": {
+    "window_name": "G1 Camera Recorder",
+    "camera_backend": "CAP_V4L2",
+    "camera_probe_attempts": 3,
+    "camera_fps_threshold": 1.0,
+    "track_history_maxlen": 25,
+    "selected_track_history_key": "selected",
+    "display_env_var": "DISPLAY",
+    "dependency_messages": {
+      "opencv_missing": "OpenCV is not installed for this python. Install it or run the script in the same environment you use for /home/zedx/Robotics_workspace/AI/YOLO/YOLO_Test_3.py.",
+      "ultralytics_missing": "ultralytics is not installed for this python. Install it or run the script in the same environment you use for /home/zedx/Robotics_workspace/AI/YOLO/YOLO_Test_3.py."
+    }
+  },
+  "defaults": {
+    "source": "ask",
+    "camera_scan_max": 10,
+    "output": "test_take",
+    "seconds": 0.0,
+    "record_hz": 20.0,
+    "conf": 0.25,
+    "kpt_conf": 0.35,
+    "imgsz": 640,
+    "smoothing": 0.35,
+    "window_width": 1100,
+    "window_height": 700,
+    "show": true,
+    "no_prompt": false
+  },
+  "replay_defaults": {
+    "iface": "enp3s0",
+    "input": "test_take.jsonl",
+    "home": "arm_home.jsonl",
+    "speed": 0.35,
+    "max_arm_delta": 0.18,
+    "gap_warn_seconds": 0.35,
+    "abort_gap_seconds": 0.0,
+    "allow_track_switch": false,
+    "start_steps": 60,
+    "home_steps": 180,
+    "no_prompt": false
+  },
+  "output": {
+    "dataset_suffix": ".jsonl",
+    "raw_pose_suffix": ".pose.jsonl",
+    "dataset_meta_format": "g1_camera_pose_v1",
+    "raw_meta_format": "g1_camera_pose_raw_v1",
+    "dataset_notes": "Lower body fixed to home pose; upper body derived heuristically from 2D YOLO keypoints."
+  },
+  "selection": {
+    "area_confidence_bias": 0.6
+  },
+  "pose_ui": {
+    "joint_names": {
+      "5": "L_shoulder",
+      "6": "R_shoulder",
+      "7": "L_elbow",
+      "8": "R_elbow",
+      "9": "L_wrist",
+      "10": "R_wrist",
+      "11": "L_hip",
+      "12": "R_hip",
+      "13": "L_knee",
+      "14": "R_knee",
+      "15": "L_ankle",
+      "16": "R_ankle"
+    },
+    "skeleton_edges": [
+      [5, 7],
+      [7, 9],
+      [6, 8],
+      [8, 10],
+      [5, 6],
+      [5, 11],
+      [6, 12],
+      [11, 12],
+      [11, 13],
+      [13, 15],
+      [12, 14],
+      [14, 16]
+    ]
+  }
+}
diff --git a/Camera_Recorder/camera_recorder_config.py b/Camera_Recorder/camera_recorder_config.py
new file mode 100644
index 0000000..d616131
--- /dev/null
+++ b/Camera_Recorder/camera_recorder_config.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+
+
+def load_config(config_path: str | Path = "camera_recorder_config.json") -> tuple[dict[str, Any], Path]:
+    path = Path(config_path)
+    if not path.is_absolute():
+        if path.exists():
+            path = path.resolve()
+        else:
+            path = (SCRIPT_DIR / path).resolve()
+
+    with path.open("r", encoding="utf-8") as handle:
+        return json.load(handle), path.parent
+
+
+def load_json_file(path: str | Path) -> dict[str, Any]:
+    with Path(path).open("r", encoding="utf-8") as handle:
+        return json.load(handle)
+
+
+def resolve_path(config_dir: Path, raw_path: str | Path) -> Path:
+    path = Path(raw_path)
+    if path.is_absolute():
+        return path
+    if path.exists():
+        return path.resolve()
+    return (config_dir / path).resolve()
+
+
+def resolve_default_model(config: dict[str, Any], config_dir: Path) -> str:
+    path_cfg = config["paths"]
+    for raw_path in path_cfg["default_model_candidates"]:
+        candidate = resolve_path(config_dir, raw_path)
+        if candidate.exists():
+            return str(candidate)
+    return path_cfg["model_fallback"]
+
+
+def load_mapper_configs(config: dict[str, Any], config_dir: Path) -> tuple[dict[str, Any], dict[str, Any], Path, Path]:
+    path_cfg = config["paths"]
+    joint_path = resolve_path(config_dir, path_cfg["joint_config"])
+    pose_mapping_path = resolve_path(config_dir, path_cfg["pose_mapping_config"])
+    return (
+        load_json_file(joint_path),
+        load_json_file(pose_mapping_path),
+        joint_path,
+        pose_mapping_path,
+    )
diff --git a/Camera_Recorder/g1_camera_pose_mapper.py b/Camera_Recorder/g1_camera_pose_mapper.py
new file mode 100644
index 0000000..3dd94d2
--- /dev/null
+++ b/Camera_Recorder/g1_camera_pose_mapper.py
@@ -0,0 +1,318 @@
+from __future__ import annotations
+
+import copy
+import json
+import math
+from pathlib import Path
+from typing import Any, Iterable, Mapping, Sequence
+
+
+def clamp(value: float, low: float, high: float) -> float:
+    return max(low, min(high, float(value)))
+
+
+def lerp(a: float, b: float, ratio: float) -> float:
+    return (1.0 - ratio) * float(a) + ratio * float(b)
+
+
+def vec_sub(a: tuple[float, float], b: tuple[float, float]) -> tuple[float, float]:
+    return (a[0] - b[0], a[1] - b[1])
+
+
+def vec_mid(a: tuple[float, float], b: tuple[float, float]) -> tuple[float, float]:
+    return ((a[0] + b[0]) * 0.5, (a[1] + b[1]) * 0.5)
+
+
+def vec_len(a: tuple[float, float]) -> float:
+    return math.hypot(a[0], a[1])
+
+
+def vec_norm(a: tuple[float, float], fallback: tuple[float, float]) -> tuple[float, float]:
+    length = vec_len(a)
+    if length <= 1e-6:
+        return fallback
+    return (a[0] / length, a[1] / length)
+
+
+def vec_dot(a: tuple[float, float], b: tuple[float, float]) -> float:
+    return a[0] * b[0] + a[1] * b[1]
+
+
+def angle_between(a: tuple[float, float], b: tuple[float, float], fallback_axis: tuple[float, float]) -> float:
+    na = vec_norm(a, fallback_axis)
+    nb = vec_norm(b, fallback_axis)
+    return math.acos(clamp(vec_dot(na, nb), -1.0, 1.0))
+
+
+def load_home_pose(path: str | Path, expected_joints: int) -> list[float]:
+    last_q: list[float] | None = None
+    with open(path, "r", encoding="utf-8") as handle:
+        for line in handle:
+            try:
+                data = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            q = data.get("q")
+            if isinstance(q, list) and len(q) == expected_joints:
+                last_q = [float(v) for v in q]
+
+    if last_q is None:
+        raise ValueError(f"No valid {expected_joints}-joint pose found in {path}")
+    return last_q
+
+
+class PoseToG1Mapper:
+    """
+    Converts 2D YOLO pose keypoints into a replay-compatible G1 joint frame.
+    """
+
+    def __init__(
+        self,
+        home_q: Sequence[float],
+        joint_config: Mapping[str, Any],
+        pose_mapping_config: Mapping[str, Any],
+        *,
+        min_conf: float | None = None,
+        smoothing: float | None = None,
+    ) -> None:
+        self.joint_config = dict(joint_config)
+        self.pose_mapping_config = dict(pose_mapping_config)
+        self.num_motors = int(self.joint_config["g1_num_motor"])
+        if len(home_q) != self.num_motors:
+            raise ValueError(f"Expected {self.num_motors} joints in home_q, got {len(home_q)}")
+
+        self.home_q = [float(v) for v in home_q]
+        self.prev_q = list(self.home_q)
+
+        waist_cfg = self.joint_config["waist_joint_indices"]
+        self.waist_yaw = int(waist_cfg["yaw"])
+        self.waist_roll = int(waist_cfg["roll"])
+        self.waist_pitch = int(waist_cfg["pitch"])
+
+        self.joint_limits = {
+            int(idx): (float(bounds[0]), float(bounds[1]))
+            for idx, bounds in self.joint_config["joint_limits"].items()
+        }
+        self.keypoint_ids = {
+            name: int(value)
+            for name, value in self.pose_mapping_config["body_keypoint_indices"].items()
+        }
+        self.serializable_indices = [int(idx) for idx in self.pose_mapping_config["serializable_keypoint_indices"]]
+        fallback_cfg = self.pose_mapping_config["fallback_vectors"]
+        self.axis_x_fallback = tuple(float(v) for v in fallback_cfg["axis_x"])
+        self.torso_up_fallback = tuple(float(v) for v in fallback_cfg["torso_up"])
+        self.diagnostics_defaults = copy.deepcopy(self.pose_mapping_config["diagnostics_defaults"])
+        self.min_segment_length = float(self.pose_mapping_config["min_segment_length_px"])
+        self.waist_map = self.pose_mapping_config["waist"]
+        self.arm_maps = self.pose_mapping_config["arms"]
+
+        default_min_conf = float(self.pose_mapping_config["min_conf"])
+        default_smoothing = float(self.pose_mapping_config["default_smoothing"])
+        self.min_conf = float(default_min_conf if min_conf is None else min_conf)
+        self.smoothing = clamp(float(default_smoothing if smoothing is None else smoothing), 0.0, 1.0)
+
+    def reset(self) -> None:
+        self.prev_q = list(self.home_q)
+
+    def convert(
+        self,
+        keypoints: Mapping[int, Sequence[float]] | None,
+    ) -> tuple[list[float], dict[str, float | int | bool | None]]:
+        target_q = list(self.home_q)
+        tracked_joint_indices: set[int] = set()
+
+        diagnostics: dict[str, float | int | bool | None] = copy.deepcopy(self.diagnostics_defaults)
+
+        if not keypoints:
+            self.prev_q = list(self.home_q)
+            return list(self.home_q), diagnostics
+
+        visible = {idx: self._point(keypoints.get(idx)) for idx in self.serializable_indices}
+        diagnostics["visible_keypoints"] = sum(1 for value in visible.values() if value is not None)
+
+        left_shoulder = visible.get(self.keypoint_ids["left_shoulder"])
+        right_shoulder = visible.get(self.keypoint_ids["right_shoulder"])
+        left_hip = visible.get(self.keypoint_ids["left_hip"])
+        right_hip = visible.get(self.keypoint_ids["right_hip"])
+
+        if left_shoulder and right_shoulder:
+            shoulder_center = vec_mid(left_shoulder, right_shoulder)
+            shoulder_axis = vec_norm(vec_sub(right_shoulder, left_shoulder), self.axis_x_fallback)
+
+            if left_hip and right_hip:
+                hip_center = vec_mid(left_hip, right_hip)
+                torso_up = vec_norm(vec_sub(shoulder_center, hip_center), self.torso_up_fallback)
+                center_offset = clamp(
+                    (shoulder_center[0] - hip_center[0]) / max(vec_len(vec_sub(right_shoulder, left_shoulder)), 1.0),
+                    -1.0,
+                    1.0,
+                )
+                yaw_gain = float(self.waist_map["yaw_center_gain"])
+                target_q[self.waist_yaw] = self._joint(self.waist_yaw, self.home_q[self.waist_yaw] + yaw_gain * center_offset)
+                tracked_joint_indices.add(self.waist_yaw)
+            else:
+                torso_up = self.torso_up_fallback
+
+            diagnostics["tracked"] = True
+            diagnostics["torso_ready"] = True
+            roll_gain = float(self.waist_map["roll_shoulder_gain"])
+            target_q[self.waist_roll] = self._joint(self.waist_roll, self.home_q[self.waist_roll] + roll_gain * shoulder_axis[1])
+            tracked_joint_indices.add(self.waist_roll)
+
+            left_q, left_info = self._arm_from_pose(
+                side="left",
+                shoulder=left_shoulder,
+                elbow=visible.get(self.keypoint_ids["left_elbow"]),
+                wrist=visible.get(self.keypoint_ids["left_wrist"]),
+                torso_up=torso_up,
+                shoulder_axis=shoulder_axis,
+            )
+            if left_q is not None:
+                for idx, value in left_q.items():
+                    target_q[idx] = value
+                    tracked_joint_indices.add(idx)
+                diagnostics["left_ready"] = True
+                diagnostics["left_lift"] = left_info["lift"]
+                diagnostics["left_bend"] = left_info["bend"]
+
+            right_q, right_info = self._arm_from_pose(
+                side="right",
+                shoulder=right_shoulder,
+                elbow=visible.get(self.keypoint_ids["right_elbow"]),
+                wrist=visible.get(self.keypoint_ids["right_wrist"]),
+                torso_up=torso_up,
+                shoulder_axis=shoulder_axis,
+            )
+            if right_q is not None:
+                for idx, value in right_q.items():
+                    target_q[idx] = value
+                    tracked_joint_indices.add(idx)
+                diagnostics["right_ready"] = True
+                diagnostics["right_lift"] = right_info["lift"]
+                diagnostics["right_bend"] = right_info["bend"]
+
+        smoothed = []
+        for idx, (prev, new) in enumerate(zip(self.prev_q, target_q)):
+            if idx not in tracked_joint_indices:
+                smoothed.append(self.home_q[idx])
+                continue
+            blended = lerp(prev, new, self.smoothing)
+            if idx in self.joint_limits:
+                blended = self._joint(idx, blended)
+            smoothed.append(blended)
+
+        self.prev_q = smoothed
+        return list(smoothed), diagnostics
+
+    def _point(self, value: Sequence[float] | None) -> tuple[float, float] | None:
+        if value is None or len(value) < 3:
+            return None
+        x, y, conf = float(value[0]), float(value[1]), float(value[2])
+        if conf < self.min_conf:
+            return None
+        return (x, -y)
+
+    def _joint(self, idx: int, value: float) -> float:
+        low, high = self.joint_limits[idx]
+        return clamp(value, low, high)
+
+    def _arm_from_pose(
+        self,
+        *,
+        side: str,
+        shoulder: tuple[float, float] | None,
+        elbow: tuple[float, float] | None,
+        wrist: tuple[float, float] | None,
+        torso_up: tuple[float, float],
+        shoulder_axis: tuple[float, float],
+    ) -> tuple[dict[int, float] | None, dict[str, float]]:
+        if not shoulder or not elbow or not wrist:
+            return None, {"lift": 0.0, "bend": 0.0}
+
+        upper = vec_sub(elbow, shoulder)
+        fore = vec_sub(wrist, elbow)
+        hand = vec_sub(wrist, shoulder)
+        if vec_len(upper) < self.min_segment_length or vec_len(fore) < self.min_segment_length:
+            return None, {"lift": 0.0, "bend": 0.0}
+
+        arm_cfg = self.arm_maps[side]
+        joint_indices = {name: int(idx) for name, idx in arm_cfg["joint_indices"].items()}
+
+        upper_dir = vec_norm(upper, torso_up)
+        fore_dir = vec_norm(fore, upper_dir)
+        hand_dir = vec_norm(hand, upper_dir)
+
+        side_sign = -1.0 if side == "left" else 1.0
+        lift = clamp(vec_dot(upper_dir, torso_up), -1.0, 1.0)
+        outward = clamp(side_sign * vec_dot(upper_dir, shoulder_axis), -1.0, 1.0)
+        cross_body = clamp(-side_sign * vec_dot(hand_dir, shoulder_axis), -1.0, 1.0)
+        fore_lift = clamp(vec_dot(fore_dir, torso_up), -1.0, 1.0)
+        bend_ratio = angle_between(upper_dir, fore_dir, self.axis_x_fallback) / math.pi
+        horizontal = 1.0 - abs(lift)
+
+        shoulder_pitch_cfg = arm_cfg["shoulder_pitch"]
+        shoulder_roll_cfg = arm_cfg["shoulder_roll"]
+        shoulder_yaw_cfg = arm_cfg["shoulder_yaw"]
+        elbow_cfg = arm_cfg["elbow"]
+        wrist_roll_cfg = arm_cfg["wrist_roll"]
+        wrist_pitch_cfg = arm_cfg["wrist_pitch"]
+        wrist_yaw_cfg = arm_cfg["wrist_yaw"]
+
+        q = {
+            joint_indices["shoulder_pitch"]: self._joint(
+                joint_indices["shoulder_pitch"],
+                lerp(float(shoulder_pitch_cfg["down"]), float(shoulder_pitch_cfg["up"]), (lift + 1.0) * 0.5),
+            ),
+            joint_indices["shoulder_roll"]: self._joint(
+                joint_indices["shoulder_roll"],
+                self.home_q[joint_indices["shoulder_roll"]]
+                + float(shoulder_roll_cfg["outward_horizontal"]) * outward * horizontal
+                + float(shoulder_roll_cfg["lift_positive"]) * max(lift, 0.0)
+                + float(shoulder_roll_cfg["cross_body"]) * cross_body,
+            ),
+            joint_indices["shoulder_yaw"]: self._joint(
+                joint_indices["shoulder_yaw"],
+                self.home_q[joint_indices["shoulder_yaw"]]
+                + float(shoulder_yaw_cfg["cross_body"]) * cross_body
+                + float(shoulder_yaw_cfg["fore_lift"]) * fore_lift,
+            ),
+            joint_indices["elbow"]: self._joint(
+                joint_indices["elbow"],
+                float(elbow_cfg["base"]) + float(elbow_cfg["bend"]) * bend_ratio,
+            ),
+            joint_indices["wrist_roll"]: self._joint(
+                joint_indices["wrist_roll"],
+                self.home_q[joint_indices["wrist_roll"]]
+                + float(wrist_roll_cfg["outward"]) * outward
+                + float(wrist_roll_cfg["cross_body"]) * cross_body,
+            ),
+            joint_indices["wrist_pitch"]: self._joint(
+                joint_indices["wrist_pitch"],
+                self.home_q[joint_indices["wrist_pitch"]] + float(wrist_pitch_cfg["fore_lift"]) * fore_lift,
+            ),
+            joint_indices["wrist_yaw"]: self._joint(
+                joint_indices["wrist_yaw"],
+                self.home_q[joint_indices["wrist_yaw"]] + float(wrist_yaw_cfg["cross_body"]) * cross_body,
+            ),
+        }
+
+        return q, {"lift": lift, "bend": bend_ratio}
+
+
+def keypoints_to_serializable(
+    keypoints: Mapping[int, Sequence[float]] | None,
+    indices: Iterable[int],
+) -> dict[str, list[float] | None]:
+    serializable: dict[str, list[float] | None] = {}
+    if not keypoints:
+        for idx in indices:
+            serializable[str(idx)] = None
+        return serializable
+
+    for idx in indices:
+        point = keypoints.get(idx)
+        if point is None:
+            serializable[str(idx)] = None
+            continue
+        serializable[str(idx)] = [float(point[0]), float(point[1]), float(point[2])]
+    return serializable
diff --git a/Camera_Recorder/g1_camera_pose_recorder.py b/Camera_Recorder/g1_camera_pose_recorder.py
new file mode 100644
index 0000000..c6d63c5
--- /dev/null
+++ b/Camera_Recorder/g1_camera_pose_recorder.py
@@ -0,0 +1,712 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import contextlib
+from collections import defaultdict, deque
+import glob
+import json
+import math
+import os
+import sys
+import time
+from pathlib import Path
+from typing import Any
+
+from camera_recorder_config import load_config, load_mapper_configs, resolve_default_model, resolve_path
+from g1_camera_pose_mapper import PoseToG1Mapper, keypoints_to_serializable, load_home_pose
+
+
+cv2 = None
+YOLO = None
+
+
+def clamp(value: float, low: float, high: float) -> float:
+    return max(low, min(high, float(value)))
+
+
+def ensure_runtime_dependencies(runtime_cfg: dict[str, Any]) -> None:
+    global cv2, YOLO
+
+    messages = runtime_cfg["dependency_messages"]
+
+    if cv2 is None:
+        try:
+            import cv2 as _cv2
+        except ModuleNotFoundError as exc:
+            raise SystemExit(messages["opencv_missing"]) from exc
+        cv2 = _cv2
+
+    if YOLO is None:
+        try:
+            from ultralytics import YOLO as _YOLO
+        except ModuleNotFoundError as exc:
+            raise SystemExit(messages["ultralytics_missing"]) from exc
+        YOLO = _YOLO
+
+
+def parse_source(source: str) -> int | str:
+    return int(source) if source.isdigit() else source
+
+
+def wait_for_confirmation(prompt: str, enabled: bool = True) -> bool:
+    if not enabled or not sys.stdin.isatty():
+        return True
+    try:
+        input(prompt)
+        return True
+    except KeyboardInterrupt:
+        print("\n[REC] Ctrl+C pressed.")
+        return False
+
+
+def ensure_unique_path(user_path: str, default_dir: Path, suffix: str) -> Path:
+    path = Path(user_path)
+    if not path.suffix:
+        path = path.with_suffix(suffix)
+
+    if len(path.parts) == 1:
+        default_dir.mkdir(parents=True, exist_ok=True)
+        candidate = default_dir / path.name
+    else:
+        candidate = path
+        candidate.parent.mkdir(parents=True, exist_ok=True)
+
+    if not candidate.exists():
+        return candidate
+
+    index = 1
+    while True:
+        alt = candidate.with_name(f"{candidate.stem}({index}){candidate.suffix}")
+        if not alt.exists():
+            return alt
+        index += 1
+
+
+def ensure_unique_output_pair(
+    user_path: str,
+    dataset_dir: Path,
+    dataset_suffix: str,
+    raw_pose_dir: Path,
+    raw_pose_suffix: str,
+) -> tuple[Path, Path]:
+    dataset_path = Path(user_path)
+    if not dataset_path.suffix:
+        dataset_path = dataset_path.with_suffix(dataset_suffix)
+
+    if len(dataset_path.parts) == 1:
+        dataset_dir.mkdir(parents=True, exist_ok=True)
+        dataset_candidate = dataset_dir / dataset_path.name
+    else:
+        dataset_candidate = dataset_path
+        dataset_candidate.parent.mkdir(parents=True, exist_ok=True)
+
+    raw_pose_dir.mkdir(parents=True, exist_ok=True)
+
+    def raw_candidate_for(path: Path) -> Path:
+        return raw_pose_dir / f"{path.stem}{raw_pose_suffix}"
+
+    raw_candidate = raw_candidate_for(dataset_candidate)
+    if not dataset_candidate.exists() and not raw_candidate.exists():
+        return dataset_candidate, raw_candidate
+
+    index = 1
+    while True:
+        dataset_alt = dataset_candidate.with_name(f"{dataset_candidate.stem}({index}){dataset_candidate.suffix}")
+        raw_alt = raw_candidate_for(dataset_alt)
+        if not dataset_alt.exists() and not raw_alt.exists():
+            return dataset_alt, raw_alt
+        index += 1
+
+
+def _opencv_error_log_level() -> Any:
+    if hasattr(cv2, "LOG_LEVEL_ERROR"):
+        return cv2.LOG_LEVEL_ERROR
+    if hasattr(cv2, "utils") and hasattr(cv2.utils, "logging"):
+        return cv2.utils.logging.LOG_LEVEL_ERROR
+    return None
+
+
+@contextlib.contextmanager
+def suppress_camera_probe_noise():
+    old_level = None
+    try:
+        if hasattr(cv2, "getLogLevel"):
+            old_level = cv2.getLogLevel()
+        error_level = _opencv_error_log_level()
+        if error_level is not None and hasattr(cv2, "setLogLevel"):
+            cv2.setLogLevel(error_level)
+    except Exception:
+        old_level = None
+
+    with open(os.devnull, "w", encoding="utf-8") as devnull:
+        with contextlib.redirect_stderr(devnull):
+            try:
+                yield
+            finally:
+                try:
+                    if old_level is not None and hasattr(cv2, "setLogLevel"):
+                        cv2.setLogLevel(old_level)
+                except Exception:
+                    pass
+
+
+def camera_indices_from_dev(max_index: int) -> list[int]:
+    indices = []
+    for path in glob.glob("/dev/video*"):
+        suffix = path.replace("/dev/video", "")
+        if suffix.isdigit():
+            index = int(suffix)
+            if index <= max_index:
+                indices.append(index)
+    return sorted(set(indices))
+
+
+def resolve_camera_backend(runtime_cfg: dict[str, Any]) -> int:
+    backend_name = runtime_cfg["camera_backend"]
+    return int(getattr(cv2, backend_name, getattr(cv2, "CAP_ANY", 0)))
+
+
+def _try_open_camera(index: int, runtime_cfg: dict[str, Any]) -> dict[str, float | int] | None:
+    backend = resolve_camera_backend(runtime_cfg)
+    probe_attempts = int(runtime_cfg["camera_probe_attempts"])
+    fps_threshold = float(runtime_cfg["camera_fps_threshold"])
+
+    cap = cv2.VideoCapture(index, backend)
+    if not cap.isOpened():
+        cap.release()
+        return None
+
+    ok = False
+    frame = None
+    for _ in range(probe_attempts):
+        ok, frame = cap.read()
+        if ok and frame is not None and frame.size > 0:
+            break
+
+    if not ok or frame is None:
+        cap.release()
+        return None
+
+    fps = float(cap.get(cv2.CAP_PROP_FPS) or 0.0)
+    if fps <= fps_threshold or math.isnan(fps):
+        fps = 0.0
+
+    info = {
+        "index": index,
+        "width": int(frame.shape[1]),
+        "height": int(frame.shape[0]),
+        "fps": fps,
+    }
+    cap.release()
+    return info
+
+
+def list_available_cameras(max_index: int, runtime_cfg: dict[str, Any]) -> list[dict[str, float | int]]:
+    candidates = camera_indices_from_dev(max_index)
+    if not candidates:
+        candidates = list(range(max_index + 1))
+
+    cameras = []
+    with suppress_camera_probe_noise():
+        for index in candidates:
+            info = _try_open_camera(index, runtime_cfg)
+            if info is not None:
+                cameras.append(info)
+    return cameras
+
+
+def choose_camera(cameras: list[dict[str, float | int]], runtime_cfg: dict[str, Any]) -> int:
+    if len(cameras) == 1:
+        return int(cameras[0]["index"])
+
+    fps_threshold = float(runtime_cfg["camera_fps_threshold"])
+
+    print("\nAvailable cameras:")
+    for option_num, cam in enumerate(cameras, start=1):
+        fps = float(cam["fps"])
+        fps_text = f"{fps:.1f}fps" if fps > fps_threshold and not math.isnan(fps) else "fps unknown"
+        print(
+            f"  {option_num}. camera {int(cam['index'])} "
+            f"({int(cam['width'])}x{int(cam['height'])}, {fps_text})"
+        )
+
+    if not sys.stdin.isatty():
+        return int(cameras[0]["index"])
+
+    while True:
+        try:
+            choice = input("Choose camera option number: ").strip()
+        except KeyboardInterrupt as exc:
+            raise SystemExit("\n[REC] Camera selection cancelled by Ctrl+C.") from exc
+        if choice.isdigit():
+            selected = int(choice)
+            if 1 <= selected <= len(cameras):
+                return int(cameras[selected - 1]["index"])
+        print("Invalid choice. Enter a valid option number.")
+
+
+def open_capture(source: int | str, runtime_cfg: dict[str, Any]):
+    backend = resolve_camera_backend(runtime_cfg)
+    if isinstance(source, int):
+        cap = cv2.VideoCapture(source, backend)
+        if cap.isOpened():
+            return cap
+    return cv2.VideoCapture(source)
+
+
+def extract_people(result, kpt_conf: float) -> list[dict[str, Any]]:
+    boxes = result.boxes
+    kpts = result.keypoints
+    if boxes is None or kpts is None or len(boxes) == 0:
+        return []
+
+    ids = boxes.id
+    if ids is not None:
+        track_ids = [int(value) for value in ids.int().cpu().tolist()]
+    else:
+        track_ids = [None] * len(boxes)
+
+    xyxy = boxes.xyxy.cpu().tolist()
+    box_conf = boxes.conf.cpu().tolist() if boxes.conf is not None else [0.0] * len(boxes)
+    kpts_xy = kpts.xy.cpu().numpy()
+    kpts_conf = kpts.conf.cpu().numpy() if kpts.conf is not None else None
+
+    people = []
+    for index in range(len(boxes)):
+        x1, y1, x2, y2 = [float(v) for v in xyxy[index]]
+        kp_map: dict[int, tuple[float, float, float]] = {}
+        confs = []
+        for kp_idx in range(len(kpts_xy[index])):
+            conf = float(kpts_conf[index][kp_idx]) if kpts_conf is not None else 1.0
+            x, y = float(kpts_xy[index][kp_idx][0]), float(kpts_xy[index][kp_idx][1])
+            kp_map[kp_idx] = (x, y, conf)
+            if conf >= kpt_conf:
+                confs.append(conf)
+
+        area = max(1.0, (x2 - x1) * (y2 - y1))
+        people.append(
+            {
+                "track_id": track_ids[index],
+                "bbox": [x1, y1, x2, y2],
+                "det_conf": float(box_conf[index]),
+                "mean_kpt_conf": float(sum(confs) / len(confs)) if confs else 0.0,
+                "area": area,
+                "keypoints": kp_map,
+            }
+        )
+    return people
+
+
+def select_person(
+    people: list[dict[str, Any]],
+    locked_track_id: int | None,
+    selection_cfg: dict[str, Any],
+) -> dict[str, Any] | None:
+    if not people:
+        return None
+
+    if locked_track_id is not None:
+        for person in people:
+            if person["track_id"] == locked_track_id:
+                return person
+
+    bias = float(selection_cfg["area_confidence_bias"])
+    return max(
+        people,
+        key=lambda person: float(person["area"]) * (bias + float(person["mean_kpt_conf"])),
+    )
+
+
+def draw_people(
+    frame,
+    people: list[dict[str, Any]],
+    selected_person: dict[str, Any] | None,
+    track_history: dict[str, deque[tuple[int, int]]],
+    kpt_conf: float,
+    pose_ui_cfg: dict[str, Any],
+    runtime_cfg: dict[str, Any],
+) -> Any:
+    selected_key = str(runtime_cfg["selected_track_history_key"])
+    selected_id = selected_person["track_id"] if selected_person else None
+    joint_names = {int(idx): label for idx, label in pose_ui_cfg["joint_names"].items()}
+    skeleton_edges = [tuple(int(v) for v in edge) for edge in pose_ui_cfg["skeleton_edges"]]
+
+    for person in people:
+        bbox = person["bbox"]
+        keypoints = person["keypoints"]
+        is_selected = person is selected_person or (
+            selected_id is not None and person["track_id"] == selected_id
+        )
+
+        line_color = (0, 220, 255) if is_selected else (110, 110, 110)
+        text_color = (255, 255, 255) if is_selected else (180, 180, 180)
+        point_color = (0, 64, 255) if is_selected else (140, 140, 140)
+        thickness = 2 if is_selected else 1
+
+        x1, y1, x2, y2 = [int(v) for v in bbox]
+        cv2.rectangle(frame, (x1, y1), (x2, y2), line_color, thickness)
+
+        valid_points = []
+        for a, b in skeleton_edges:
+            pa = keypoints.get(a)
+            pb = keypoints.get(b)
+            if pa is None or pb is None:
+                continue
+            if pa[2] < kpt_conf or pb[2] < kpt_conf:
+                continue
+            p1 = (int(pa[0]), int(pa[1]))
+            p2 = (int(pb[0]), int(pb[1]))
+            valid_points.extend([p1, p2])
+            cv2.line(frame, p1, p2, line_color, thickness)
+
+        for idx, label in joint_names.items():
+            point = keypoints.get(idx)
+            if point is None or point[2] < kpt_conf:
+                continue
+            px, py = int(point[0]), int(point[1])
+            valid_points.append((px, py))
+            cv2.circle(frame, (px, py), 4 if is_selected else 3, point_color, -1)
+            if is_selected:
+                cv2.putText(
+                    frame,
+                    label,
+                    (px + 5, py - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.35,
+                    text_color,
+                    1,
+                    cv2.LINE_AA,
+                )
+
+        if is_selected and valid_points:
+            cx = int(sum(point[0] for point in valid_points) / len(valid_points))
+            cy = int(sum(point[1] for point in valid_points) / len(valid_points))
+            history = track_history[selected_key]
+            history.append((cx, cy))
+            while len(history) > history.maxlen:
+                history.popleft()
+            for index in range(1, len(history)):
+                cv2.line(frame, history[index - 1], history[index], (0, 255, 0), 2)
+
+        label = f"ID {person['track_id']}" if person["track_id"] is not None else "person"
+        cv2.putText(
+            frame,
+            label,
+            (x1 + 5, max(18, y1 - 8)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.55,
+            text_color,
+            2 if is_selected else 1,
+            cv2.LINE_AA,
+        )
+
+    return frame
+
+
+def add_overlay(
+    frame,
+    *,
+    elapsed: float,
+    samples: int,
+    selected_person: dict[str, Any] | None,
+    diagnostics: dict[str, Any],
+    output_name: str,
+) -> None:
+    lines = [
+        f"REC {elapsed:6.2f}s  samples={samples}",
+        f"target={output_name}",
+        (
+            f"track={selected_person['track_id']} "
+            f"left={int(bool(diagnostics.get('left_ready')))} "
+            f"right={int(bool(diagnostics.get('right_ready')))}"
+        )
+        if selected_person is not None
+        else "track=none  waiting for pose",
+        "Press q or ESC to stop",
+    ]
+
+    for idx, line in enumerate(lines):
+        y = 28 + idx * 24
+        cv2.putText(frame, line, (12, y), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (16, 16, 16), 4, cv2.LINE_AA)
+        cv2.putText(frame, line, (12, y), cv2.FONT_HERSHEY_SIMPLEX, 0.65, (255, 255, 255), 1, cv2.LINE_AA)
+
+
+def build_parser(config: dict[str, Any], config_path: str) -> argparse.ArgumentParser:
+    defaults = config["defaults"]
+    parser = argparse.ArgumentParser(
+        description=(
+            "Capture human pose from a camera with YOLO and save a replay-compatible G1 dataset."
+        )
+    )
+    parser.add_argument("--config", default=config_path, help="Path to camera_recorder_config.json")
+    parser.add_argument(
+        "--source",
+        default=defaults["source"],
+        help="Video source: webcam index, path, or 'ask' to scan available cameras.",
+    )
+    parser.add_argument(
+        "--camera-scan-max",
+        type=int,
+        default=int(defaults["camera_scan_max"]),
+        help="Max camera index to scan when --source ask is used.",
+    )
+    parser.add_argument(
+        "--model",
+        default=None,
+        help="YOLO pose model path/name. Defaults to the first model candidate found in camera_recorder_config.json.",
+    )
+    parser.add_argument(
+        "--output",
+        "--output-file",
+        "--output-name",
+        dest="output",
+        default=defaults["output"],
+        help="Output dataset name/path.",
+    )
+    parser.add_argument(
+        "--home-pose",
+        "--home",
+        "--home-file",
+        default=config["paths"]["default_home_pose"],
+        dest="home_pose",
+        help="Home pose jsonl used for lower body and fallback upper body values.",
+    )
+    parser.add_argument("--seconds", type=float, default=float(defaults["seconds"]), help="0 = record until q / ESC.")
+    parser.add_argument("--record-hz", type=float, default=float(defaults["record_hz"]), help="Max dataset sample rate.")
+    parser.add_argument("--conf", type=float, default=float(defaults["conf"]), help="Detection confidence threshold.")
+    parser.add_argument("--kpt-conf", type=float, default=float(defaults["kpt_conf"]), help="Keypoint confidence threshold.")
+    parser.add_argument("--imgsz", type=int, default=int(defaults["imgsz"]), help="Inference image size.")
+    parser.add_argument("--smoothing", type=float, default=float(defaults["smoothing"]), help="Low-pass blend for mapped joints.")
+    parser.add_argument("--window-width", type=int, default=int(defaults["window_width"]), help="GUI window width.")
+    parser.add_argument("--window-height", type=int, default=int(defaults["window_height"]), help="GUI window height.")
+    parser.add_argument("--show", dest="show", action="store_true", help="Show the live camera window.")
+    parser.add_argument("--no-show", dest="show", action="store_false", help="Disable the live GUI.")
+    parser.add_argument(
+        "--no-prompt",
+        action="store_true",
+        default=bool(defaults.get("no_prompt", False)),
+        help="Skip the Enter-to-begin confirmation prompt.",
+    )
+    parser.set_defaults(show=bool(defaults["show"]))
+    return parser
+
+
+def parse_args_and_config() -> tuple[argparse.Namespace, dict[str, Any], Path]:
+    pre_parser = argparse.ArgumentParser(add_help=False)
+    pre_parser.add_argument("--config", default="camera_recorder_config.json")
+    pre_args, _ = pre_parser.parse_known_args()
+
+    config, config_dir = load_config(pre_args.config)
+    parser = build_parser(config, pre_args.config)
+    args = parser.parse_args()
+    return args, config, config_dir
+
+
+def main() -> None:
+    args, config, config_dir = parse_args_and_config()
+    runtime_cfg = config["runtime"]
+    output_cfg = config["output"]
+    selection_cfg = config["selection"]
+    pose_ui_cfg = config["pose_ui"]
+    joint_cfg, pose_mapping_cfg, joint_cfg_path, pose_mapping_cfg_path = load_mapper_configs(config, config_dir)
+
+    ensure_runtime_dependencies(runtime_cfg)
+
+    data_dir = resolve_path(config_dir, config["paths"]["data_dir"])
+    raw_pose_dir = resolve_path(config_dir, config["paths"]["raw_pose_dir"])
+    model_path = args.model if args.model else resolve_default_model(config, config_dir)
+    home_pose_path = resolve_path(config_dir, args.home_pose)
+
+    output_path, raw_output_path = ensure_unique_output_pair(
+        args.output,
+        data_dir,
+        output_cfg["dataset_suffix"],
+        raw_pose_dir,
+        output_cfg["raw_pose_suffix"],
+    )
+
+    display_env_var = str(runtime_cfg["display_env_var"])
+    if args.show and os.name != "nt" and not os.environ.get(display_env_var):
+        print(f"{display_env_var} not found. Disabling GUI window.")
+        args.show = False
+
+    home_q = load_home_pose(home_pose_path, int(joint_cfg["g1_num_motor"]))
+    mapper = PoseToG1Mapper(
+        home_q,
+        joint_cfg,
+        pose_mapping_cfg,
+        min_conf=args.kpt_conf,
+        smoothing=args.smoothing,
+    )
+    model = YOLO(model_path)
+
+    if str(args.source).lower() == "ask":
+        cameras = list_available_cameras(args.camera_scan_max, runtime_cfg)
+        if not cameras:
+            raise SystemExit("No camera found. Connect a camera or pass --source <path/index>.")
+        source = choose_camera(cameras, runtime_cfg)
+        print(f"Using camera index: {source}")
+    else:
+        source = parse_source(str(args.source))
+
+    cap = open_capture(source, runtime_cfg)
+    if not cap.isOpened():
+        raise SystemExit(f"Could not open source: {source}")
+
+    if args.show:
+        cv2.namedWindow(str(runtime_cfg["window_name"]), cv2.WINDOW_NORMAL)
+        cv2.resizeWindow(
+            str(runtime_cfg["window_name"]),
+            max(320, args.window_width),
+            max(240, args.window_height),
+        )
+
+    meta = {
+        "meta": {
+            "format": output_cfg["dataset_meta_format"],
+            "created_unix": time.time(),
+            "motors": mapper.num_motors,
+            "source": str(source),
+            "model": str(model_path),
+            "record_hz": float(args.record_hz),
+            "home_pose": str(home_pose_path),
+            "raw_pose_file": str(raw_output_path.resolve()),
+            "config_file": str(resolve_path(config_dir, args.config)),
+            "joint_config_file": str(joint_cfg_path),
+            "pose_mapping_file": str(pose_mapping_cfg_path),
+            "notes": output_cfg["dataset_notes"],
+        }
+    }
+    raw_meta = {
+        "meta": {
+            "format": output_cfg["raw_meta_format"],
+            "created_unix": time.time(),
+            "source": str(source),
+            "model": str(model_path),
+            "kpt_conf": float(args.kpt_conf),
+            "dataset_file": str(output_path.resolve()),
+            "config_file": str(resolve_path(config_dir, args.config)),
+            "joint_config_file": str(joint_cfg_path),
+            "pose_mapping_file": str(pose_mapping_cfg_path),
+        }
+    }
+
+    selected_track_id = None
+    history_key = str(runtime_cfg["selected_track_history_key"])
+    history_len = int(runtime_cfg["track_history_maxlen"])
+    track_history: dict[str, deque[tuple[int, int]]] = defaultdict(lambda: deque(maxlen=history_len))
+    last_sample_wall = 0.0
+    last_status_wall = 0.0
+    samples = 0
+    started_wall = time.time()
+
+    print(f"[REC] Dataset  -> {output_path}")
+    print(f"[REC] Raw pose -> {raw_output_path}")
+    print("[REC] Press q or ESC to stop.")
+
+    if not wait_for_confirmation("👉 Press Enter to Begin recording... ", enabled=not args.no_prompt):
+        cap.release()
+        if args.show:
+            cv2.destroyAllWindows()
+        print("[REC] Recording cancelled before start.")
+        return
+
+    with output_path.open("w", encoding="utf-8") as dataset_file, raw_output_path.open("w", encoding="utf-8") as raw_file:
+        dataset_file.write(json.dumps(meta) + "\n")
+        raw_file.write(json.dumps(raw_meta) + "\n")
+
+        try:
+            while True:
+                ok, frame = cap.read()
+                if not ok:
+                    break
+
+                result = model.track(
+                    source=frame,
+                    conf=args.conf,
+                    imgsz=args.imgsz,
+                    persist=True,
+                    verbose=False,
+                )[0]
+                people = extract_people(result, args.kpt_conf)
+                selected_person = select_person(people, selected_track_id, selection_cfg)
+                if selected_person is not None and selected_person["track_id"] is not None:
+                    selected_track_id = int(selected_person["track_id"])
+                elif not people:
+                    selected_track_id = None
+                    track_history[history_key].clear()
+
+                keypoints = selected_person["keypoints"] if selected_person is not None else None
+                q_frame, diagnostics = mapper.convert(keypoints)
+
+                now = time.time()
+                elapsed = now - started_wall
+
+                sample_dt = 0.0 if args.record_hz <= 0 else (1.0 / args.record_hz)
+                should_store = sample_dt == 0.0 or (now - last_sample_wall) >= sample_dt
+                if should_store:
+                    dataset_row = {
+                        "t": round(elapsed, 6),
+                        "q": [round(float(v), 6) for v in q_frame],
+                        "tracked": bool(diagnostics["tracked"]),
+                        "track_id": selected_person["track_id"] if selected_person is not None else None,
+                    }
+                    raw_row = {
+                        "t": round(elapsed, 6),
+                        "tracked": bool(diagnostics["tracked"]),
+                        "track_id": selected_person["track_id"] if selected_person is not None else None,
+                        "bbox": selected_person["bbox"] if selected_person is not None else None,
+                        "det_conf": selected_person["det_conf"] if selected_person is not None else 0.0,
+                        "mean_kpt_conf": selected_person["mean_kpt_conf"] if selected_person is not None else 0.0,
+                        "diagnostics": diagnostics,
+                        "keypoints": keypoints_to_serializable(keypoints, mapper.serializable_indices),
+                    }
+                    dataset_file.write(json.dumps(dataset_row) + "\n")
+                    raw_file.write(json.dumps(raw_row) + "\n")
+                    samples += 1
+                    last_sample_wall = now
+
+                if args.show:
+                    display = draw_people(
+                        frame,
+                        people,
+                        selected_person,
+                        track_history,
+                        args.kpt_conf,
+                        pose_ui_cfg,
+                        runtime_cfg,
+                    )
+                    add_overlay(
+                        display,
+                        elapsed=elapsed,
+                        samples=samples,
+                        selected_person=selected_person,
+                        diagnostics=diagnostics,
+                        output_name=output_path.name,
+                    )
+                    cv2.imshow(str(runtime_cfg["window_name"]), display)
+                    key = cv2.waitKey(1) & 0xFF
+                    if key in (27, ord("q")):
+                        break
+                elif now - last_status_wall >= 1.0:
+                    status = "tracked" if diagnostics["tracked"] else "waiting"
+                    print(f"[REC] {elapsed:6.2f}s samples={samples} state={status}")
+                    last_status_wall = now
+
+                if args.seconds > 0 and elapsed >= args.seconds:
+                    break
+
+        except KeyboardInterrupt:
+            print("\n[REC] Stopped by user.")
+        finally:
+            cap.release()
+            if args.show:
+                cv2.destroyAllWindows()
+
+    print(f"[REC] Saved {samples} samples to {output_path}")
+    print(f"[REC] Saved raw pose log to {raw_output_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Camera_Recorder/g1_camera_pose_replay.py b/Camera_Recorder/g1_camera_pose_replay.py
new file mode 100644
index 0000000..cb7f8dd
--- /dev/null
+++ b/Camera_Recorder/g1_camera_pose_replay.py
@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+"""
+G1 REPLAY V14 (HOME RETURN + FULL BODY LOCK)
+--------------------------------------------
+1. LOCK: Legs/Waist Rigid (Kp=300) to support weight.
+2. REPLAY: Plays your recorded motion.
+3. RETURN: Smoothly moves arms to 'arm_home.jsonl' pose at the end.
+   - Ignores leg data from home file (keeps them locked for balance).
+   - Slow 3-second transition for safety.
+
+Usage:
+  python3 g1_camera_pose_replay.py --iface enp3s0 --input my_teleop_data.jsonl --home arm_home.jsonl
+  
+  
+  python3 g1_camera_pose_replay.py
+"""
+
+import time
+import sys
+import json
+import argparse
+import numpy as np
+from pathlib import Path
+from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowState_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_
+from unitree_sdk2py.utils.crc import CRC
+
+# ✅ added (for pause/resume keys)
+import termios
+import tty
+import select
+
+from camera_recorder_config import load_config
+
+G1_NUM_MOTOR = 29
+ENABLE_ARM_SDK_INDEX = 29
+DATA_DIR = Path("DataG1")
+REPLAY_HZ = 60.0
+
+# --- GAINS (ROBOT_ARM.PY STANDARD) ---
+KP_HIGH = 300.0  # Core/Legs
+KD_HIGH = 3.0
+KP_LOW  = 80.0   # Arms/Ankles
+KD_LOW  = 3.0
+KP_WRIST = 40.0
+KD_WRIST = 1.5
+
+WEAK_MOTORS = [4, 10, 15, 16, 17, 18, 22, 23, 24, 25]
+WRIST_MOTORS = [19, 20, 21, 26, 27, 28]
+
+def resolve_input_path(in_path: str) -> str:
+    p = Path(in_path)
+    if len(p.parts) == 1: return str(DATA_DIR / p.name)
+    return str(p)
+
+def load_home_pose(home_path: str):
+    """Reads the last frame of arm_home.jsonl to get the target pose."""
+    path = resolve_input_path(home_path)
+    try:
+        last_valid_q = None
+        with open(path, 'r') as f:
+            for line in f:
+                d = json.loads(line)
+                if 'q' in d and len(d['q']) == G1_NUM_MOTOR:
+                    last_valid_q = d['q']
+        if last_valid_q:
+            print(f"✅ Loaded Home Pose from {path}")
+            return last_valid_q
+        else:
+            print(f"⚠️  Warning: {path} found but contained no valid 'q' data.")
+    except FileNotFoundError:
+        print(f"⚠️  Warning: Home file {path} not found.")
+    
+    # Fallback: Zero Arms
+    print("⚠️  Using Default Home (Arms at 0.0)")
+    default_q = [0.0] * G1_NUM_MOTOR
+    return default_q
+
+# ✅ added: non-blocking key reader (x pause, z play)
+class KeyPoller:
+    def __enter__(self):
+        self.enabled = sys.stdin.isatty()
+        if not self.enabled:
+            self.fd = None
+            self.old_settings = None
+            return self
+        self.fd = sys.stdin.fileno()
+        self.old_settings = termios.tcgetattr(self.fd)
+        tty.setcbreak(self.fd)  # immediate key reads
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        if self.enabled and self.fd is not None and self.old_settings is not None:
+            termios.tcsetattr(self.fd, termios.TCSADRAIN, self.old_settings)
+
+    def poll(self):
+        if not self.enabled or self.fd is None:
+            return None
+        if select.select([sys.stdin], [], [], 0)[0]:
+            return sys.stdin.read(1)
+        return None
+
+class ReplayWithHome:
+    def __init__(self):
+        self.low_state = None
+        self.low_cmd = unitree_hg_msg_dds__LowCmd_()
+        self.crc = CRC()
+        self.arm_pub = ChannelPublisher("rt/arm_sdk", LowCmd_)
+        self.arm_pub.Init()
+        self.state_sub = ChannelSubscriber("rt/lowstate", LowState_)
+        self.state_sub.Init(self.LowStateHandler, 10)
+        self.first_state = False
+
+    def LowStateHandler(self, msg: LowState_):
+        self.low_state = msg
+        self.first_state = True
+
+    def SendFrame(self, arm_target_q, body_lock_q):
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 1.0
+        
+        for i in range(G1_NUM_MOTOR):
+            self.low_cmd.motor_cmd[i].mode = 1
+            self.low_cmd.motor_cmd[i].dq = 0
+            self.low_cmd.motor_cmd[i].tau = 0
+            
+            # --- POSITIONS ---
+            # Arms (15-28) -> Follow Target (Replay or Home)
+            # Body (0-14)  -> Follow Lock (Statue Mode)
+            if i >= 15: 
+                self.low_cmd.motor_cmd[i].q = arm_target_q[i]
+            else:       
+                self.low_cmd.motor_cmd[i].q = body_lock_q[i]
+
+            # --- GAINS ---
+            if i in WEAK_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_LOW
+                self.low_cmd.motor_cmd[i].kd = KD_LOW
+            elif i in WRIST_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_WRIST
+                self.low_cmd.motor_cmd[i].kd = KD_WRIST
+            else:
+                self.low_cmd.motor_cmd[i].kp = KP_HIGH # 300.0
+                self.low_cmd.motor_cmd[i].kd = KD_HIGH
+
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        self.arm_pub.Write(self.low_cmd)
+
+    def DisableSDK(self):
+        print("\n🔌 Disabling SDK...")
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 0.0
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        for _ in range(10):
+            self.arm_pub.Write(self.low_cmd)
+            time.sleep(0.02)
+
+    def Run(self, filename: str, home_filename: str, speed: float, no_prompt: bool):
+        print("Waiting for robot...", end="", flush=True)
+        while not self.first_state: time.sleep(0.1)
+        print(" Connected!")
+
+        # 1. LOAD DATA
+        home_q = load_home_pose(home_filename)
+        full_body_lock_q = [self.low_state.motor_state[i].q for i in range(G1_NUM_MOTOR)]
+        
+        frames = []
+        try:
+            with open(filename, 'r') as f:
+                for line in f:
+                    d = json.loads(line)
+                    if 'q' in d: frames.append(d)
+        except Exception as e: print(f"Error: {e}"); return
+        
+        print(f"🟢 Ready to play {len(frames)} frames.")
+        print(f"🔒 Body is LOCKED. Arms will return to 'arm_home' at end.")
+        print("🎮 Controls:  x = PAUSE   z = PLAY/RESUME   q = QUIT")
+        if sys.stdin.isatty() and not no_prompt:
+            input("👉 Press Enter to Begin...")
+
+        # 2. MOVE TO START
+        print("Moving to start...")
+        file_start_q = frames[0]['q']
+        steps = 60
+        for k in range(steps):
+            alpha = k / steps
+            interp_q = list(full_body_lock_q)
+            for j in range(15, 29):
+                interp_q[j] = (1-alpha)*full_body_lock_q[j] + alpha*file_start_q[j]
+            self.SendFrame(interp_q, full_body_lock_q)
+            time.sleep(1.0/REPLAY_HZ)
+
+        # 3. PLAY REPLAY (with pause/resume)
+        print("▶️ Playing... (press x to pause)")
+        last_played_q = file_start_q
+        paused = False
+
+        # Instead of using (time.time() - t_start) directly, we use a play clock
+        # that only advances when not paused.
+        play_elapsed = 0.0
+        last_real = time.time()
+
+        try:
+            with KeyPoller() as kp:
+                while True:
+                    # --- key handling ---
+                    key = kp.poll()
+                    if key:
+                        key = key.lower()
+                        if key == 'x' and not paused:
+                            paused = True
+                            print("\n⏸️  PAUSED (press z to continue)")
+                        elif key == 'z' and paused:
+                            paused = False
+                            last_real = time.time()  # reset real-time anchor
+                            print("\n▶️ RESUMED")
+                        elif key == 'q':
+                            print("\n🛑 QUIT")
+                            break
+
+                    now_real = time.time()
+
+                    if paused:
+                        # Hold last pose, do not advance play time
+                        self.SendFrame(last_played_q, full_body_lock_q)
+                        time.sleep(1.0 / REPLAY_HZ)
+                        continue
+
+                    # advance play clock only when running
+                    dt_real = now_real - last_real
+                    last_real = now_real
+                    play_elapsed += dt_real * speed
+
+                    target_frame = None
+                    for f in frames:
+                        if f['t'] - frames[0]['t'] >= play_elapsed:
+                            target_frame = f
+                            break
+                    if target_frame is None:
+                        break
+
+                    self.SendFrame(target_frame['q'], full_body_lock_q)
+                    last_played_q = target_frame['q']
+                    time.sleep(1.0 / REPLAY_HZ)
+
+        except KeyboardInterrupt:
+            print("\nStopped.")
+
+        # 4. RETURN TO ARM HOME (Slow & Smooth)
+        print(f"\n🏡 Returning arms to {home_filename}...")
+        
+        # 3 Seconds duration for smoothness
+        home_steps = 180 
+        
+        for k in range(home_steps):
+            alpha = k / home_steps
+            # Interpolate: Last Pose -> Home Pose
+            interp_q = list(last_played_q)
+            for j in range(15, 29):
+                interp_q[j] = (1-alpha)*last_played_q[j] + alpha*home_q[j]
+            
+            # Send (Body still locked to original standing pose)
+            self.SendFrame(interp_q, full_body_lock_q)
+            time.sleep(1.0/REPLAY_HZ)
+
+        print("✅ Home Reached.")
+        self.DisableSDK()
+
+if __name__ == "__main__":
+    pre_parser = argparse.ArgumentParser(add_help=False)
+    pre_parser.add_argument("--config", default="camera_recorder_config.json")
+    pre_args, _ = pre_parser.parse_known_args()
+    config, _ = load_config(pre_args.config)
+    replay_defaults = config.get("replay_defaults", {})
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=pre_args.config, help="Path to camera_recorder_config.json")
+    parser.add_argument("iface_pos", nargs="?", default=None, help="Network interface")
+    parser.add_argument("--iface", default=None, help="Network interface")
+    parser.add_argument(
+        "--input",
+        "--input-file",
+        dest="input",
+        default=replay_defaults.get("input"),
+        help="Input recording file",
+    )
+    parser.add_argument(
+        "--home",
+        "--home-file",
+        dest="home",
+        default=replay_defaults.get("home", "arm_home.jsonl"),
+        help="Home pose file",
+    )
+    parser.add_argument("--speed", type=float, default=float(replay_defaults.get("speed", 1.0)))
+    parser.add_argument(
+        "--no-prompt",
+        action="store_true",
+        default=bool(replay_defaults.get("no_prompt", False)),
+        help="Skip the Enter-to-begin prompt",
+    )
+    args = parser.parse_args()
+
+    args.iface = args.iface or args.iface_pos or replay_defaults.get("iface")
+
+    if not args.iface:
+        parser.error("Missing interface. Pass it on the command line or set replay_defaults.iface in camera_recorder_config.json.")
+    if not args.input:
+        parser.error("Missing input file. Pass --input or set replay_defaults.input in camera_recorder_config.json.")
+
+    ChannelFactoryInitialize(0, args.iface)
+    path = resolve_input_path(args.input)
+    ReplayWithHome().Run(path, args.home, args.speed, args.no_prompt)
diff --git a/Camera_Recorder/g1_camera_pose_replay_v2.py b/Camera_Recorder/g1_camera_pose_replay_v2.py
new file mode 100644
index 0000000..f3e4bb1
--- /dev/null
+++ b/Camera_Recorder/g1_camera_pose_replay_v2.py
@@ -0,0 +1,764 @@
+#!/usr/bin/env python3
+"""
+g1_camera_pose_replay_v2.py  —  Enhanced
+
+Original v2 features:
+  • Skip rows with invalid/missing 29-joint data
+  • Skip rows where tracked=false
+  • Lock to first valid track_id by default
+  • Clamp sudden arm jumps between accepted frames
+  • Hold last good pose when frames are skipped
+  • Preflight summary before moving the robot
+
+Enhancements in this version:
+  • Joint limit enforcement  — loads joint.json limits, clamps out-of-range values
+                               and reports violation count in preflight
+  • Frame interpolation      — linearly interpolates between source frames at 60 Hz,
+                               eliminating step-and-hold jitter at low recording rates
+  • Live progress bar        — shows elapsed / total time and frame counter during playback
+  • Loop mode  (--loop)      — replay the file repeatedly until q is pressed
+  • Dry-run    (--dry-run)   — full preflight and validation without touching the robot
+  • Fix duplicate timestamps — skips frames with t <= last accepted t (was t < last_t)
+  • Progress feedback        — shows % completion during start / home interpolation phases
+  • Cleaner preflight report — adds duration, playback ETA, limit violation counts
+"""
+
+from __future__ import annotations
+
+import argparse
+from dataclasses import dataclass, field
+import json
+import math
+from pathlib import Path
+import select
+import sys
+import termios
+import time
+import tty
+
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize, ChannelPublisher, ChannelSubscriber
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowState_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_
+from unitree_sdk2py.utils.crc import CRC
+
+from camera_recorder_config import load_config
+from g1_camera_pose_mapper import load_home_pose
+
+
+# ---------------------------------------------------------------------------
+# Constants
+# ---------------------------------------------------------------------------
+
+G1_NUM_MOTOR         = 29
+ENABLE_ARM_SDK_INDEX = 29
+DATA_DIR             = Path("DataG1")
+REPLAY_HZ            = 60.0
+ARM_START            = 15
+ARM_END              = 29
+
+KP_HIGH  = 300.0
+KD_HIGH  = 3.0
+KP_LOW   = 80.0
+KD_LOW   = 3.0
+KP_WRIST = 40.0
+KD_WRIST = 1.5
+
+WEAK_MOTORS  = {4, 10, 15, 16, 17, 18, 22, 23, 24, 25}
+WRIST_MOTORS = {19, 20, 21, 26, 27, 28}
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def resolve_input_path(in_path: str) -> str:
+    path = Path(in_path)
+    if not path.suffix:
+        path = path.with_suffix(".jsonl")
+    if len(path.parts) == 1:
+        return str(DATA_DIR / path.name)
+    return str(path)
+
+
+def clamp(value: float, low: float, high: float) -> float:
+    return max(low, min(high, float(value)))
+
+
+def lerp(a: float, b: float, t: float) -> float:
+    return (1.0 - t) * float(a) + t * float(b)
+
+
+def interp_q(a: list[float], b: list[float], t: float) -> list[float]:
+    """Linearly interpolate between two full joint position vectors."""
+    return [lerp(a[i], b[i], t) for i in range(len(a))]
+
+
+def arm_delta(a: list[float], b: list[float]) -> float:
+    return max(abs(float(a[i]) - float(b[i])) for i in range(ARM_START, ARM_END))
+
+
+def clamp_arm_step(
+    target_q: list[float], prev_q: list[float], max_delta: float
+) -> tuple[list[float], bool]:
+    clamped_q   = list(target_q)
+    was_clamped = False
+    for i in range(ARM_START, ARM_END):
+        delta = float(target_q[i]) - float(prev_q[i])
+        if abs(delta) > max_delta:
+            clamped_q[i] = float(prev_q[i]) + math.copysign(max_delta, delta)
+            was_clamped  = True
+    return clamped_q, was_clamped
+
+
+def apply_joint_limits(
+    q: list[float], limits: dict[int, tuple[float, float]]
+) -> tuple[list[float], int]:
+    """Clamp q to physical joint limits. Returns (clamped_q, number_of_violations)."""
+    out        = list(q)
+    violations = 0
+    for idx, (lo, hi) in limits.items():
+        val     = float(out[idx])
+        clamped = clamp(val, lo, hi)
+        if clamped != val:
+            violations += 1
+            out[idx]    = clamped
+    return out, violations
+
+
+def load_joint_limits_from_config(config: dict) -> dict[int, tuple[float, float]]:
+    """Load joint limits from the joint.json path referenced in config."""
+    joint_config_path = config.get("paths", {}).get("joint_config", "joint.json")
+    p = Path(joint_config_path)
+    if not p.is_absolute():
+        p = Path(__file__).parent / p
+    try:
+        with open(p, "r", encoding="utf-8") as fh:
+            jdata = json.load(fh)
+        return {
+            int(k): (float(v[0]), float(v[1]))
+            for k, v in jdata.get("joint_limits", {}).items()
+        }
+    except Exception as exc:
+        print(f"⚠️  Could not load joint limits from {p}: {exc}")
+        return {}
+
+
+# ---------------------------------------------------------------------------
+# Data classes
+# ---------------------------------------------------------------------------
+
+@dataclass
+class PlaybackFrame:
+    t:                float
+    q:                list[float]
+    track_id:         int | None = None
+    source_line:      int        = 0
+    tracked:          bool       = True
+    clamped:          bool       = False
+    limit_violations: int        = 0
+
+
+@dataclass
+class PreflightReport:
+    meta:                   dict         = field(default_factory=dict)
+    raw_rows:               int          = 0
+    accepted_frames:        int          = 0
+    skipped_bad_q:          int          = 0
+    skipped_untracked:      int          = 0
+    skipped_track_switch:   int          = 0
+    skipped_non_monotonic:  int          = 0
+    skipped_non_finite:     int          = 0
+    clamped_frames:         int          = 0
+    limit_violation_frames: int          = 0
+    total_limit_violations: int          = 0
+    locked_track_id:        int | None   = None
+    track_ids_seen:         set[int]     = field(default_factory=set)
+    max_raw_arm_delta:      float        = 0.0
+    max_clean_arm_delta:    float        = 0.0
+    max_gap_seconds:        float        = 0.0
+    long_gap_count:         int          = 0
+    first_time:             float | None = None
+    last_time:              float | None = None
+
+
+# ---------------------------------------------------------------------------
+# Home pose loader
+# ---------------------------------------------------------------------------
+
+def load_home_pose_for_replay(home_path: str) -> list[float]:
+    path = resolve_input_path(home_path)
+    try:
+        q = load_home_pose(path, G1_NUM_MOTOR)
+        print(f"✅ Loaded home pose from {path}")
+        return q
+    except FileNotFoundError:
+        print(f"⚠️  Home file not found: {path}")
+    except ValueError:
+        print(f"⚠️  {path} contained no valid 29-joint pose.")
+    print("⚠️  Using default home (all zeros).")
+    return [0.0] * G1_NUM_MOTOR
+
+
+# ---------------------------------------------------------------------------
+# Dataset sanitization
+# ---------------------------------------------------------------------------
+
+def sanitize_frames(
+    filename:         str,
+    *,
+    max_arm_delta:    float,
+    gap_warn_seconds: float,
+    lock_first_track: bool,
+    joint_limits:     dict[int, tuple[float, float]],
+) -> tuple[list[PlaybackFrame], PreflightReport]:
+    report          = PreflightReport()
+    frames:          list[PlaybackFrame] = []
+    last_accepted_t: float | None        = None
+    last_good:       PlaybackFrame | None = None
+
+    with open(filename, "r", encoding="utf-8") as fh:
+        for line_num, raw_line in enumerate(fh, start=1):
+            line = raw_line.strip()
+            if not line:
+                continue
+            try:
+                data = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+
+            # metadata line
+            if "meta" in data and "q" not in data:
+                report.meta = data.get("meta", {})
+                continue
+
+            report.raw_rows += 1
+
+            # ── q validation ──────────────────────────────────────────────
+            q = data.get("q")
+            if not isinstance(q, list) or len(q) != G1_NUM_MOTOR:
+                report.skipped_bad_q += 1
+                continue
+            try:
+                qf = [float(v) for v in q]
+            except (TypeError, ValueError):
+                report.skipped_bad_q += 1
+                continue
+            if any(not math.isfinite(v) for v in qf):
+                report.skipped_non_finite += 1
+                continue
+
+            # ── strict monotonic timestamp (skips equal too — fixes duplicates) ──
+            t = float(data.get("t", report.raw_rows / REPLAY_HZ))
+            if last_accepted_t is not None and t <= last_accepted_t:
+                report.skipped_non_monotonic += 1
+                continue
+
+            # ── tracking ──────────────────────────────────────────────────
+            track_id_raw = data.get("track_id")
+            track_id     = int(track_id_raw) if isinstance(track_id_raw, int) else None
+            if track_id is not None:
+                report.track_ids_seen.add(track_id)
+
+            tracked = bool(data.get("tracked", True))
+            if not tracked:
+                report.skipped_untracked += 1
+                continue
+
+            if lock_first_track and track_id is not None:
+                if report.locked_track_id is None:
+                    report.locked_track_id = track_id
+                elif track_id != report.locked_track_id:
+                    report.skipped_track_switch += 1
+                    continue
+
+            # ── joint limit enforcement ───────────────────────────────────
+            q_limited, violations = apply_joint_limits(qf, joint_limits)
+            if violations > 0:
+                report.limit_violation_frames += 1
+                report.total_limit_violations += violations
+
+            # ── arm jump clamping ─────────────────────────────────────────
+            q_clean     = q_limited
+            was_clamped = False
+            if last_good is not None:
+                raw_delta            = arm_delta(q_limited, last_good.q)
+                report.max_raw_arm_delta = max(report.max_raw_arm_delta, raw_delta)
+                q_clean, was_clamped = clamp_arm_step(q_limited, last_good.q, max_arm_delta)
+                report.max_clean_arm_delta = max(
+                    report.max_clean_arm_delta, arm_delta(q_clean, last_good.q)
+                )
+                gap                  = t - last_good.t
+                report.max_gap_seconds = max(report.max_gap_seconds, gap)
+                if gap_warn_seconds > 0 and gap > gap_warn_seconds:
+                    report.long_gap_count += 1
+
+            if was_clamped:
+                report.clamped_frames += 1
+
+            if report.first_time is None:
+                report.first_time = t
+            report.last_time  = t
+            last_accepted_t   = t
+
+            frame = PlaybackFrame(
+                t=t,
+                q=q_clean,
+                track_id=track_id,
+                source_line=line_num,
+                tracked=tracked,
+                clamped=was_clamped,
+                limit_violations=violations,
+            )
+            frames.append(frame)
+            last_good = frame
+
+    report.accepted_frames = len(frames)
+    return frames, report
+
+
+# ---------------------------------------------------------------------------
+# Preflight report
+# ---------------------------------------------------------------------------
+
+def print_preflight(filename: str, report: PreflightReport, args) -> None:
+    duration     = (
+        (report.last_time - report.first_time)
+        if report.first_time is not None and report.last_time is not None
+        else 0.0
+    )
+    playback_eta = duration / max(args.speed, 1e-6)
+
+    print("\n" + "=" * 66)
+    print("📋  CAMERA REPLAY PREFLIGHT")
+    print(f"   File             : {filename}")
+    if report.meta:
+        print(f"   Format           : {report.meta.get('format', 'unknown')}")
+        print(f"   Model            : {report.meta.get('model', 'unknown')}")
+        print(f"   Recorded Hz      : {report.meta.get('hz', 'unknown')}")
+    print(f"   Duration         : {duration:.2f} s  ({report.accepted_frames} frames)")
+    print(f"   Playback ETA     : ~{playback_eta:.1f} s  at {args.speed:.2f}×")
+    print("─" * 66)
+    print(f"   Raw rows         : {report.raw_rows}")
+    print(f"   Accepted         : {report.accepted_frames}")
+    print(f"   Skipped bad q    : {report.skipped_bad_q}")
+    print(f"   Skipped non-finite: {report.skipped_non_finite}")
+    print(f"   Skipped untracked: {report.skipped_untracked}")
+    print(f"   Skipped track swap: {report.skipped_track_switch}")
+    print(f"   Skipped bad time : {report.skipped_non_monotonic}")
+    print(f"   Clamped frames   : {report.clamped_frames}  (max delta = {args.max_arm_delta:.3f} rad)")
+    if report.total_limit_violations:
+        print(
+            f"   Limit violations : {report.limit_violation_frames} frames, "
+            f"{report.total_limit_violations} joints total  ← clamped to safe range"
+        )
+    else:
+        print("   Limit violations : none  ✅")
+    print("─" * 66)
+    print(f"   Locked track id  : {report.locked_track_id}")
+    print(f"   Track ids seen   : {sorted(report.track_ids_seen) if report.track_ids_seen else 'none'}")
+    print(f"   Max raw arm jump : {report.max_raw_arm_delta:.4f} rad")
+    print(f"   Max clean arm jump: {report.max_clean_arm_delta:.4f} rad")
+    print(f"   Max frame gap    : {report.max_gap_seconds:.4f} s")
+    print(f"   Gap warnings     : {report.long_gap_count}  (threshold = {args.gap_warn_seconds:.3f} s)")
+    if args.abort_gap_seconds <= 0:
+        print("   Gap abort        : DISABLED  (pass --abort-gap-seconds to enable)")
+    else:
+        print(f"   Gap abort        : {args.abort_gap_seconds:.3f} s")
+    print("=" * 66 + "\n")
+
+
+# ---------------------------------------------------------------------------
+# Keyboard poller
+# ---------------------------------------------------------------------------
+
+class KeyPoller:
+    def __enter__(self):
+        self.enabled     = sys.stdin.isatty()
+        self.fd          = None
+        self.old_settings = None
+        if self.enabled:
+            self.fd           = sys.stdin.fileno()
+            self.old_settings = termios.tcgetattr(self.fd)
+            tty.setcbreak(self.fd)
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        if self.enabled and self.fd is not None and self.old_settings is not None:
+            termios.tcsetattr(self.fd, termios.TCSADRAIN, self.old_settings)
+
+    def poll(self) -> str | None:
+        if not self.enabled or self.fd is None:
+            return None
+        if select.select([sys.stdin], [], [], 0)[0]:
+            return sys.stdin.read(1)
+        return None
+
+
+# ---------------------------------------------------------------------------
+# Replay engine
+# ---------------------------------------------------------------------------
+
+class ReplayWithHomeV2:
+    def __init__(self):
+        self.low_state   = None
+        self.low_cmd     = unitree_hg_msg_dds__LowCmd_()
+        self.crc         = CRC()
+        self.arm_pub     = ChannelPublisher("rt/arm_sdk", LowCmd_)
+        self.arm_pub.Init()
+        self.state_sub   = ChannelSubscriber("rt/lowstate", LowState_)
+        self.state_sub.Init(self.LowStateHandler, 10)
+        self.first_state = False
+
+    def LowStateHandler(self, msg: LowState_) -> None:
+        self.low_state   = msg
+        self.first_state = True
+
+    def SendFrame(self, arm_target_q: list[float], body_lock_q: list[float]) -> None:
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 1.0
+        for i in range(G1_NUM_MOTOR):
+            self.low_cmd.motor_cmd[i].mode = 1
+            self.low_cmd.motor_cmd[i].dq   = 0.0
+            self.low_cmd.motor_cmd[i].tau  = 0.0
+            self.low_cmd.motor_cmd[i].q    = arm_target_q[i] if i >= ARM_START else body_lock_q[i]
+            if i in WRIST_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_WRIST
+                self.low_cmd.motor_cmd[i].kd = KD_WRIST
+            elif i in WEAK_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_LOW
+                self.low_cmd.motor_cmd[i].kd = KD_LOW
+            else:
+                self.low_cmd.motor_cmd[i].kp = KP_HIGH
+                self.low_cmd.motor_cmd[i].kd = KD_HIGH
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        self.arm_pub.Write(self.low_cmd)
+
+    def DisableSDK(self) -> None:
+        print("\n🔌 Disabling SDK...")
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 0.0
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        for _ in range(10):
+            self.arm_pub.Write(self.low_cmd)
+            time.sleep(0.02)
+
+    def _interpolate_phase(
+        self,
+        label:   str,
+        from_q:  list[float],
+        to_q:    list[float],
+        body_q:  list[float],
+        steps:   int,
+    ) -> None:
+        """Smoothly move arms from from_q → to_q over `steps` frames at REPLAY_HZ."""
+        dt = 1.0 / REPLAY_HZ
+        for step in range(steps):
+            alpha = step / max(1, steps - 1)
+            self.SendFrame(interp_q(from_q, to_q, alpha), body_q)
+            if step % 15 == 0 or step == steps - 1:
+                pct = int(100.0 * alpha)
+                bar = ("█" * (pct // 5)).ljust(20)
+                print(f"\r  {label} [{bar}] {pct:3d}%", end="", flush=True)
+            time.sleep(dt)
+        print()
+
+    def _play_once(
+        self,
+        frames:      list[PlaybackFrame],
+        body_lock_q: list[float],
+        speed:       float,
+        kp:          KeyPoller,
+    ) -> tuple[list[float], bool]:
+        """
+        Play all frames once with frame interpolation and a live progress bar.
+        Returns (last_played_q, quit_requested).
+        """
+        first_t       = frames[0].t
+        final_t       = frames[-1].t - first_t
+        frame_idx     = 0
+        play_elapsed  = 0.0
+        last_real     = time.time()
+        last_played_q = list(frames[0].q)
+        paused        = False
+        quit_req      = False
+        dt            = 1.0 / REPLAY_HZ
+
+        while True:
+            key = kp.poll()
+            if key:
+                k = key.lower()
+                if k == "x" and not paused:
+                    paused    = True
+                    print("\n⏸️  PAUSED  (z = resume, q = quit)")
+                elif k == "z" and paused:
+                    paused    = False
+                    last_real = time.time()
+                    print("\n▶️  RESUMED")
+                elif k == "q":
+                    quit_req  = True
+                    break
+
+            now_real  = time.time()
+            if paused:
+                self.SendFrame(last_played_q, body_lock_q)
+                time.sleep(dt)
+                continue
+
+            dt_real      = now_real - last_real
+            last_real    = now_real
+            play_elapsed += dt_real * speed
+
+            # advance frame index
+            while frame_idx + 1 < len(frames):
+                if frames[frame_idx + 1].t - first_t <= play_elapsed:
+                    frame_idx += 1
+                else:
+                    break
+
+            # ── interpolate between source frames (smooth 60 Hz output) ──
+            if frame_idx + 1 < len(frames):
+                fa      = frames[frame_idx]
+                fb      = frames[frame_idx + 1]
+                seg_len = max(fb.t - fa.t, 1e-9)
+                alpha   = clamp((play_elapsed - (fa.t - first_t)) / seg_len, 0.0, 1.0)
+                out_q   = interp_q(fa.q, fb.q, alpha)
+            else:
+                out_q = list(frames[frame_idx].q)
+
+            last_played_q = out_q
+            self.SendFrame(out_q, body_lock_q)
+
+            # ── live progress bar ─────────────────────────────────────────
+            pct = clamp(play_elapsed / max(final_t, 1e-9) * 100.0, 0.0, 100.0)
+            bar = ("█" * int(pct / 5)).ljust(20)
+            print(
+                f"\r  ▶ [{bar}] {pct:5.1f}%  "
+                f"{min(play_elapsed, final_t):.1f}/{final_t:.1f}s  "
+                f"frame {frame_idx + 1}/{len(frames)}  ",
+                end="",
+                flush=True,
+            )
+
+            if frame_idx >= len(frames) - 1 and play_elapsed >= final_t:
+                break
+
+            time.sleep(dt)
+
+        print()
+        return last_played_q, quit_req
+
+    def Run(self, filename: str, home_filename: str, args, config: dict) -> None:
+        # ── Joint limits ──────────────────────────────────────────────────
+        joint_limits = load_joint_limits_from_config(config)
+        if joint_limits:
+            print(f"✅ Joint limits loaded for {len(joint_limits)} joints.")
+        else:
+            print("⚠️  No joint limits loaded — limit enforcement skipped.")
+
+        # ── Sanitize dataset ──────────────────────────────────────────────
+        print(f"📂 Loading: {filename}")
+        frames, report = sanitize_frames(
+            filename,
+            max_arm_delta=args.max_arm_delta,
+            gap_warn_seconds=args.gap_warn_seconds,
+            lock_first_track=not args.allow_track_switch,
+            joint_limits=joint_limits,
+        )
+        print_preflight(filename, report, args)
+
+        if report.accepted_frames < 2:
+            raise SystemExit("❌ Not enough valid frames after filtering. Replay aborted.")
+        if args.abort_gap_seconds > 0 and report.max_gap_seconds > args.abort_gap_seconds:
+            raise SystemExit(
+                f"❌ Replay aborted: max gap {report.max_gap_seconds:.3f}s exceeds "
+                f"--abort-gap-seconds {args.abort_gap_seconds:.3f}s"
+            )
+
+        # ── Connect to robot ──────────────────────────────────────────────
+        print("Waiting for robot state...", end="", flush=True)
+        while not self.first_state:
+            time.sleep(0.1)
+        print(" Connected!\n")
+
+        home_q      = load_home_pose_for_replay(home_filename)
+        body_lock_q = [self.low_state.motor_state[i].q for i in range(G1_NUM_MOTOR)]
+
+        print("🔒 Body LOCKED. Arms will follow filtered camera data.")
+        print("🎮 Controls:  x = PAUSE   z = RESUME   q = QUIT\n")
+        if sys.stdin.isatty() and not args.no_prompt:
+            input("👉 Press Enter to Begin... ")
+
+        # ── Move to start pose ────────────────────────────────────────────
+        self._interpolate_phase(
+            "Moving to start pose",
+            body_lock_q, frames[0].q,
+            body_lock_q, args.start_steps,
+        )
+        last_played_q = list(frames[0].q)
+
+        # ── Playback (with optional loop) ─────────────────────────────────
+        loop_num = 0
+        try:
+            with KeyPoller() as kp:
+                while True:
+                    loop_num += 1
+                    if args.loop:
+                        print(f"▶️  Loop {loop_num}  (x = pause, q = quit)")
+                    else:
+                        print("▶️  Playing...  (x = pause, q = quit)")
+
+                    last_played_q, quit_req = self._play_once(
+                        frames, body_lock_q, args.speed, kp
+                    )
+                    if quit_req or not args.loop:
+                        break
+                    time.sleep(0.3)
+        except KeyboardInterrupt:
+            print("\nStopped.")
+
+        # ── Return to home ────────────────────────────────────────────────
+        print(f"\n🏡 Returning to home  ({home_filename})...")
+        self._interpolate_phase(
+            "Returning to home",
+            last_played_q, home_q,
+            body_lock_q, args.home_steps,
+        )
+        print("✅ Home reached.")
+        self.DisableSDK()
+
+
+# ---------------------------------------------------------------------------
+# Dry-run path  (preflight only, no SDK)
+# ---------------------------------------------------------------------------
+
+def run_dry(filename: str, home_filename: str, args, config: dict) -> None:
+    joint_limits = load_joint_limits_from_config(config)
+    if joint_limits:
+        print(f"✅ Joint limits loaded for {len(joint_limits)} joints.")
+    else:
+        print("⚠️  No joint limits loaded.")
+
+    print(f"📂 Loading: {filename}")
+    frames, report = sanitize_frames(
+        filename,
+        max_arm_delta=args.max_arm_delta,
+        gap_warn_seconds=args.gap_warn_seconds,
+        lock_first_track=not args.allow_track_switch,
+        joint_limits=joint_limits,
+    )
+    print_preflight(filename, report, args)
+
+    if report.accepted_frames < 2:
+        raise SystemExit("❌ Not enough valid frames after filtering.")
+    if args.abort_gap_seconds > 0 and report.max_gap_seconds > args.abort_gap_seconds:
+        raise SystemExit(
+            f"❌ Aborted: max gap {report.max_gap_seconds:.3f}s exceeds "
+            f"--abort-gap-seconds {args.abort_gap_seconds:.3f}s"
+        )
+    print("✅ Dry-run complete. No robot connection made.")
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+
+def main() -> None:
+    pre_parser = argparse.ArgumentParser(add_help=False)
+    pre_parser.add_argument("--config", default="camera_recorder_config.json")
+    pre_args, _ = pre_parser.parse_known_args()
+    config, _   = load_config(pre_args.config)
+    rd          = config.get("replay_defaults", {})
+
+    parser = argparse.ArgumentParser(
+        description="Camera pose replay for G1 humanoid — enhanced v2",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--config",   default=pre_args.config,        help="Config JSON path")
+    parser.add_argument("iface_pos",  nargs="?", default=None,        help="Network interface (positional)")
+    parser.add_argument("--iface",    default=None,                    help="Network interface")
+    parser.add_argument(
+        "--input", "--input-file", dest="input",
+        default=rd.get("input"),
+        help="Input JSONL recording file",
+    )
+    parser.add_argument(
+        "--home", "--home-file", dest="home",
+        default=rd.get("home", "arm_home.jsonl"),
+        help="Home pose JSONL file",
+    )
+    parser.add_argument(
+        "--speed", type=float,
+        default=float(rd.get("speed", 0.35)),
+        help="Replay speed multiplier",
+    )
+    parser.add_argument(
+        "--max-arm-delta", type=float,
+        default=float(rd.get("max_arm_delta", 0.18)),
+        help="Max allowed arm-joint delta between source frames (rad)",
+    )
+    parser.add_argument(
+        "--gap-warn-seconds", type=float,
+        default=float(rd.get("gap_warn_seconds", 0.35)),
+        help="Warn when source frame gap exceeds this (s)",
+    )
+    parser.add_argument(
+        "--abort-gap-seconds", type=float,
+        default=float(rd.get("abort_gap_seconds", 0.0)),
+        help="Abort replay if frame gap exceeds this (s). 0 = disabled.",
+    )
+    parser.add_argument(
+        "--allow-track-switch", action="store_true",
+        default=bool(rd.get("allow_track_switch", False)),
+        help="Do not lock to the first valid track_id",
+    )
+    parser.add_argument(
+        "--start-steps", type=int,
+        default=int(rd.get("start_steps", 60)),
+        help="Frames to reach start pose (@ 60 Hz = seconds × 60)",
+    )
+    parser.add_argument(
+        "--home-steps", type=int,
+        default=int(rd.get("home_steps", 180)),
+        help="Frames to return to home (@ 60 Hz = seconds × 60)",
+    )
+    parser.add_argument(
+        "--no-prompt", action="store_true",
+        default=bool(rd.get("no_prompt", False)),
+        help="Skip the Enter-to-begin prompt",
+    )
+    parser.add_argument(
+        "--loop", action="store_true",
+        default=bool(rd.get("loop", False)),
+        help="Loop replay until q is pressed",
+    )
+    parser.add_argument(
+        "--dry-run", action="store_true",
+        default=False,
+        help="Run preflight validation only — no robot connection",
+    )
+    args = parser.parse_args()
+
+    args.iface = args.iface or args.iface_pos or rd.get("iface")
+
+    if not args.dry_run and not args.iface:
+        parser.error(
+            "Missing interface. Pass it positionally, via --iface, "
+            "or set replay_defaults.iface in camera_recorder_config.json."
+        )
+    if not args.input:
+        parser.error(
+            "Missing input file. Pass --input or set replay_defaults.input "
+            "in camera_recorder_config.json."
+        )
+
+    path = resolve_input_path(args.input)
+
+    if args.dry_run:
+        run_dry(path, args.home, args, config)
+    else:
+        ChannelFactoryInitialize(0, args.iface)
+        ReplayWithHomeV2().Run(path, args.home, args, config)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Camera_Recorder/joint.json b/Camera_Recorder/joint.json
new file mode 100644
index 0000000..2a938db
--- /dev/null
+++ b/Camera_Recorder/joint.json
@@ -0,0 +1,27 @@
+{
+  "g1_num_motor": 29,
+  "waist_joint_indices": {
+    "yaw": 12,
+    "roll": 13,
+    "pitch": 14
+  },
+  "joint_limits": {
+    "12": [-0.08, 0.092],
+    "13": [-0.164, 0.112],
+    "14": [-0.131, 0.109],
+    "15": [-3.052, 0.583],
+    "16": [-0.046, 2.248],
+    "17": [-0.987, 2.45],
+    "18": [-0.868, 1.403],
+    "19": [-1.431, 0.811],
+    "20": [-1.028, 0.764],
+    "21": [-1.143, 0.737],
+    "22": [-2.968, 0.625],
+    "23": [-1.427, 0.112],
+    "24": [-0.984, 0.928],
+    "25": [-0.826, 1.495],
+    "26": [-1.188, 1.162],
+    "27": [-1.365, 0.656],
+    "28": [-0.676, 0.92]
+  }
+}
diff --git a/Camera_Recorder/pose_mapping.json b/Camera_Recorder/pose_mapping.json
new file mode 100644
index 0000000..d4610b7
--- /dev/null
+++ b/Camera_Recorder/pose_mapping.json
@@ -0,0 +1,113 @@
+{
+  "min_conf": 0.35,
+  "default_smoothing": 0.35,
+  "serializable_keypoint_indices": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+  "body_keypoint_indices": {
+    "left_shoulder": 5,
+    "right_shoulder": 6,
+    "left_elbow": 7,
+    "right_elbow": 8,
+    "left_wrist": 9,
+    "right_wrist": 10,
+    "left_hip": 11,
+    "right_hip": 12
+  },
+  "diagnostics_defaults": {
+    "tracked": false,
+    "visible_keypoints": 0,
+    "torso_ready": false,
+    "left_ready": false,
+    "right_ready": false,
+    "left_lift": null,
+    "right_lift": null,
+    "left_bend": null,
+    "right_bend": null
+  },
+  "fallback_vectors": {
+    "axis_x": [1.0, 0.0],
+    "torso_up": [0.0, 1.0]
+  },
+  "min_segment_length_px": 5.0,
+  "waist": {
+    "yaw_center_gain": 0.08,
+    "roll_shoulder_gain": 0.12
+  },
+  "arms": {
+    "left": {
+      "joint_indices": {
+        "shoulder_pitch": 15,
+        "shoulder_roll": 16,
+        "shoulder_yaw": 17,
+        "elbow": 18,
+        "wrist_roll": 19,
+        "wrist_pitch": 20,
+        "wrist_yaw": 21
+      },
+      "shoulder_pitch": {
+        "down": 0.55,
+        "up": -3.0
+      },
+      "shoulder_roll": {
+        "outward_horizontal": 1.2,
+        "lift_positive": 0.35,
+        "cross_body": -0.3
+      },
+      "shoulder_yaw": {
+        "cross_body": 0.85,
+        "fore_lift": 0.2
+      },
+      "elbow": {
+        "base": 0.65,
+        "bend": 1.05
+      },
+      "wrist_roll": {
+        "outward": 0.35,
+        "cross_body": -0.2
+      },
+      "wrist_pitch": {
+        "fore_lift": 0.45
+      },
+      "wrist_yaw": {
+        "cross_body": 0.25
+      }
+    },
+    "right": {
+      "joint_indices": {
+        "shoulder_pitch": 22,
+        "shoulder_roll": 23,
+        "shoulder_yaw": 24,
+        "elbow": 25,
+        "wrist_roll": 26,
+        "wrist_pitch": 27,
+        "wrist_yaw": 28
+      },
+      "shoulder_pitch": {
+        "down": 0.6,
+        "up": -3.0
+      },
+      "shoulder_roll": {
+        "outward_horizontal": -1.2,
+        "lift_positive": -0.35,
+        "cross_body": 0.3
+      },
+      "shoulder_yaw": {
+        "cross_body": -0.85,
+        "fore_lift": -0.2
+      },
+      "elbow": {
+        "base": 0.65,
+        "bend": 1.05
+      },
+      "wrist_roll": {
+        "outward": -0.35,
+        "cross_body": 0.2
+      },
+      "wrist_pitch": {
+        "fore_lift": 0.45
+      },
+      "wrist_yaw": {
+        "cross_body": -0.25
+      }
+    }
+  }
+}
diff --git a/Controller/Logger.py b/Controller/Logger.py
new file mode 100644
index 0000000..e75e9c7
--- /dev/null
+++ b/Controller/Logger.py
@@ -0,0 +1,134 @@
+import logging
+import os
+from datetime import datetime
+
+
+
+
+class Logs:
+
+    def __init__(self, default_log_level=logging.DEBUG, main_log_file="main.log"):
+        self.default_log_level = default_log_level
+        self.log_format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+        self.mainloggerfile = self.resolve_log_path(main_log_file)
+        self.logger = None
+
+        # Initialize the main logger
+        self.main_logger = logging.getLogger("MainLogger")
+        self.main_logger.setLevel(self.default_log_level)
+        self.main_logger.propagate = False  # Prevent logging from printing to terminal
+
+        if self.main_logger.hasHandlers():
+            self.main_logger.handlers.clear()
+
+        # Remove any StreamHandler (to avoid console logs)
+        for handler in list(self.main_logger.handlers):
+            if isinstance(handler, logging.StreamHandler):
+                self.main_logger.removeHandler(handler)
+
+        os.makedirs(os.path.dirname(self.mainloggerfile), exist_ok=True)
+        main_handler = logging.FileHandler(self.mainloggerfile)
+        main_handler.setFormatter(logging.Formatter(self.log_format))
+        main_handler.setLevel(self.default_log_level)
+        self.main_logger.addHandler(main_handler)
+
+
+    def resolve_log_path(self, path):
+        """Resolve relative or absolute path to absolute, ensuring it ends with `.log`."""
+        if not path.lower().endswith(".log"):
+            path += ".log"
+
+        if not os.path.isabs(path):
+            path = os.path.join(os.path.dirname(__file__), path)
+
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        return os.path.abspath(path)
+
+
+    def construct_path(self, folder_name, file_name):
+        """Construct full path from folder and file, respecting relative or absolute paths."""
+        if not file_name.lower().endswith(".log"):
+            file_name += ".log"
+
+        if os.path.isabs(folder_name):
+            full_path = os.path.join(folder_name, file_name)
+        else:
+            base_dir = os.path.dirname(os.path.abspath(__file__))
+            full_path = os.path.join(base_dir, folder_name, file_name)
+
+        os.makedirs(os.path.dirname(full_path), exist_ok=True)
+        return os.path.abspath(full_path)
+
+
+    def log_to_file(self, message, TypeLog):
+        level_map = {
+            "DEBUG": logging.DEBUG,
+            "INFO": logging.INFO,
+            "WARNING": logging.WARNING,
+            "ERROR": logging.ERROR,
+            "CRITICAL": logging.CRITICAL
+        }
+        log_level = level_map.get(TypeLog.upper(), logging.WARNING)
+        self.main_logger.log(log_level, message)
+
+
+    def LogEngine(self, folder_name, log_name):
+        """Set up a named logger and resolve the file path correctly."""
+        full_path = self.construct_path(folder_name, f"{log_name}.log")
+    
+        self.logger = logging.getLogger(log_name)
+        self.logger.setLevel(self.default_log_level)
+        self.logger.propagate = False  # Prevent printing to terminal
+
+        # Clear existing FileHandlers
+        for handler in self.logger.handlers[:]:
+            if isinstance(handler, logging.FileHandler):
+                self.logger.removeHandler(handler)
+
+        handler = logging.FileHandler(full_path)
+        handler.setFormatter(logging.Formatter(self.log_format))
+        handler.setLevel(self.default_log_level)
+        self.logger.addHandler(handler)
+        
+
+    def LogsMessages(self, message, message_type="info", folder_name=None, file_name=None):
+        if folder_name and file_name:
+            full_path = self.construct_path(folder_name, file_name)
+
+            temp_logger = logging.getLogger(f"{folder_name}_{file_name}")
+            temp_logger.setLevel(self.default_log_level)
+            temp_logger.propagate = False  # Prevent printing to terminal
+
+            if not any(isinstance(h, logging.FileHandler) and h.baseFilename == full_path
+                       for h in temp_logger.handlers):
+                handler = logging.FileHandler(full_path)
+                handler.setFormatter(logging.Formatter(self.log_format))
+                temp_logger.addHandler(handler)
+
+            getattr(temp_logger, message_type.lower(), temp_logger.warning)(message)
+        elif self.logger:
+            log_method = getattr(self.logger, message_type.lower(), self.logger.warning)
+            log_method(message)
+        else:
+            self.log_to_file(message, message_type.upper())
+
+    def print_and_log(self, message, message_type="info", folder_name=None, file_name=None):
+        self.LogsMessages(message, message_type, folder_name, file_name)
+        print(message)  
+
+
+
+
+
+# ==============================
+# Usage Example
+# ==============================
+if __name__ == "__main__":
+    logger = Logs()
+    logger.LogEngine("ExxxxampleLogger", "ExampleLogger.log")
+    logger.LogsMessages("This is a hidden message")
+    logger.print_and_log("This is a test message.", message_type="info")
+
+    # You can also directly specify folder and file for a log message
+    logger.print_and_log("Direct log to folder", message_type="info", folder_name="CustomLogs", file_name="event.log")
+
diff --git a/Controller/g1_arm_actions_cli.py b/Controller/g1_arm_actions_cli.py
new file mode 100644
index 0000000..bcee357
--- /dev/null
+++ b/Controller/g1_arm_actions_cli.py
@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+"""
+g1_arm_actions_cli.py
+
+Run Unitree G1 Arm ActionClient actions directly on the robot from CLI.
+
+Changes from your request:
+- Network interface is OPTIONAL (no longer required).
+  If you pass it, it will use it; otherwise it uses default DDS interface.
+
+Usage:
+  # list actions
+  python3 g1_arm_actions_cli.py list
+
+  # run by id
+  python3 g1_arm_actions_cli.py 6
+
+  # run by name (quotes recommended)
+  python3 g1_arm_actions_cli.py "face wave"
+
+  # specify interface explicitly (optional)
+  python3 g1_arm_actions_cli.py "shake hand" --iface enp3s0
+
+  # force auto release
+  python3 g1_arm_actions_cli.py "hug" --auto-release --sleep 2
+"""
+
+import argparse
+import sys
+import time
+from dataclasses import dataclass
+from typing import Optional
+
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.arm.g1_arm_action_client import G1ArmActionClient, action_map
+
+
+@dataclass
+class TestOption:
+    name: str
+    id: int
+
+
+OPTION_LIST = [
+    TestOption(name="release arm", id=0),
+    TestOption(name="shake hand", id=1),
+    TestOption(name="high five", id=2),
+    TestOption(name="hug", id=3),
+    TestOption(name="high wave", id=4),
+    TestOption(name="clap", id=5),
+    TestOption(name="face wave", id=6),
+    TestOption(name="left kiss", id=7),
+    TestOption(name="heart", id=8),
+    TestOption(name="right heart", id=9),
+    TestOption(name="hands up", id=10),
+    TestOption(name="x-ray", id=11),
+    TestOption(name="right hand up", id=12),
+    TestOption(name="reject", id=13),
+    TestOption(name="right kiss", id=14),
+    TestOption(name="two-hand kiss", id=15),
+]
+
+# Actions that your original script auto-released after ~2s
+DEFAULT_AUTO_RELEASE_IDS = {1, 2, 3, 8, 9, 10, 11, 12, 13}
+
+
+def find_option(query: str) -> Optional[TestOption]:
+    q = query.strip().lower()
+
+    # try id
+    try:
+        as_int = int(q)
+        for opt in OPTION_LIST:
+            if opt.id == as_int:
+                return opt
+    except ValueError:
+        pass
+
+    # try name
+    for opt in OPTION_LIST:
+        if opt.name.lower() == q:
+            return opt
+
+    return None
+
+
+def print_list():
+    print("\nAvailable actions:")
+    for opt in OPTION_LIST:
+        print(f"  {opt.id:>2}  {opt.name}")
+    print("")
+
+
+def execute_action(client: G1ArmActionClient, opt: TestOption):
+    if opt.name not in action_map:
+        raise RuntimeError(f"Action '{opt.name}' not found in action_map. Check SDK version.")
+    print(f"[RUN] {opt.name} (id={opt.id})")
+    client.ExecuteAction(action_map.get(opt.name))
+
+
+def init_dds(iface: Optional[str]):
+    # If iface provided, use it; otherwise default interface.
+    if iface:
+        ChannelFactoryInitialize(0, iface)
+    else:
+        ChannelFactoryInitialize(0)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "action",
+        help='Action id/name, or "list". Examples: 6  |  "face wave"  |  list',
+    )
+    ap.add_argument("--iface", default=None, help="Optional network interface, e.g. enp3s0/eth0/wlan0")
+    ap.add_argument("--timeout", type=float, default=10.0, help="Action client timeout (seconds)")
+    ap.add_argument(
+        "--auto-release",
+        action="store_true",
+        help="After action, run 'release arm' automatically (like your demo for some actions).",
+    )
+    ap.add_argument(
+        "--sleep",
+        type=float,
+        default=2.0,
+        help="Seconds to wait before auto-release (only used with auto-release).",
+    )
+    ap.add_argument(
+        "--no-prompt",
+        action="store_true",
+        help="Skip the 'Press Enter to continue' safety prompt.",
+    )
+    args = ap.parse_args()
+
+    if args.action.strip().lower() == "list":
+        print_list()
+        return
+
+    opt = find_option(args.action)
+    if not opt:
+        print(f"[ERR] No matching action for: {args.action!r}")
+        print_list()
+        sys.exit(2)
+
+    print("WARNING: Please ensure there are no obstacles around the robot while running this script.")
+    if not args.no_prompt:
+        input("Press Enter to continue... ")
+
+    # DDS init (optional iface)
+    init_dds(args.iface)
+
+    # Arm action client
+    client = G1ArmActionClient()
+    client.SetTimeout(args.timeout)
+    client.Init()
+
+    execute_action(client, opt)
+
+    # Decide auto-release behavior
+    do_auto_release = args.auto_release or (opt.id in DEFAULT_AUTO_RELEASE_IDS)
+
+    if do_auto_release and opt.id != 0:
+        time.sleep(max(0.0, args.sleep))
+        print("[AUTO] release arm")
+        client.ExecuteAction(action_map.get("release arm"))
+
+    time.sleep(0.5)
+    print("[DONE]")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Controller/g1_mode_controller.py b/Controller/g1_mode_controller.py
new file mode 100644
index 0000000..0bf535f
--- /dev/null
+++ b/Controller/g1_mode_controller.py
@@ -0,0 +1,697 @@
+#!/usr/bin/env python3
+"""
+g1_mode_controller.py
+
+Interactive terminal controller for Unitree G1 locomotion + basic upper body (waist/arms).
+
+Key features:
+- Menu is generated from a single OPTIONS dictionary (easy to extend).
+- Ctrl+C / Quit = StopMove only (keep current posture; NO prep/damp).
+- Status prints LIVE joints (deg) from LowState.
+- WALK/RUN teleop uses the "old working" keyboard control (pynput).
+- PREP mode = stand + balance (NO Start/FSM200).
+- READY/START mode = PREP + Start (FSM 200).
+- StandUp/Squat/Sit/Lie-to-Stand use SetFsmId() fallback for compatibility.
+
+Run:
+  python3 g1_mode_controller.py --iface enp3s0
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from dataclasses import dataclass
+from typing import Optional, Dict, Tuple, Callable, Any
+from threading import Lock, Event
+from collections import OrderedDict
+
+# ---------- Unitree SDK2 ----------
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize, ChannelSubscriber
+from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_  # type: ignore
+from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import MotionSwitcherClient
+
+# Optional teleop keyboard:
+try:
+    from pynput.keyboard import Listener, Key, KeyCode  # type: ignore
+    HAVE_PYNPUT = True
+except Exception:
+    HAVE_PYNPUT = False
+
+
+# ---------------- Joint map (for status) ----------------
+JOINT_NAMES = {
+    12: "WAIST_YAW",
+    13: "WAIST_ROLL",
+    14: "WAIST_PITCH",
+    15: "L_SHOULDER_PITCH",
+    16: "L_SHOULDER_ROLL",
+    17: "L_SHOULDER_YAW",
+    18: "L_ELBOW",
+    19: "L_WRIST_ROLL",
+    20: "L_WRIST_PITCH",
+    21: "L_WRIST_YAW",
+    22: "R_SHOULDER_PITCH",
+    23: "R_SHOULDER_ROLL",
+    24: "R_SHOULDER_YAW",
+    25: "R_ELBOW",
+    26: "R_WRIST_ROLL",
+    27: "R_WRIST_PITCH",
+    28: "R_WRIST_YAW",
+}
+
+
+def rad2deg(x: float) -> float:
+    return float(x) * 180.0 / 3.141592653589793
+
+
+# ---------------- Live State Monitor ----------------
+
+class LiveStateMonitor:
+    """Subscribes LowState and keeps last joint positions."""
+    def __init__(self):
+        self.lock = Lock()
+        self.last_q_rad: Dict[int, float] = {}
+        self.msg_count = 0
+        self.last_rx_time = 0.0
+        self.first_msg_evt = Event()
+
+    def cb(self, msg: LowState_):
+        motor_state = getattr(msg, "motor_state", None)
+        if motor_state is None:
+            return
+
+        with self.lock:
+            self.msg_count += 1
+            self.last_rx_time = time.time()
+
+            for idx in JOINT_NAMES.keys():
+                try:
+                    self.last_q_rad[idx] = float(motor_state[idx].q)
+                except Exception:
+                    pass
+
+        self.first_msg_evt.set()
+
+    def snapshot_deg(self) -> Tuple[int, float, Dict[int, float]]:
+        with self.lock:
+            age = (time.time() - self.last_rx_time) if self.last_rx_time else 1e9
+            qdeg = {i: rad2deg(q) for i, q in self.last_q_rad.items()}
+            return self.msg_count, age, qdeg
+
+
+# ---------------- Upper body (waist/arms) publisher ----------------
+
+@dataclass
+class ArmRig:
+    pub: object
+    cmd: object
+    crc: object
+    waist_idx: int = 12
+
+    def set_joint(self, idx: int, q: float, kp: float = 60.0, kd: float = 1.5, tau: float = 0.0):
+        mc = self.cmd.motor_cmd[idx]
+        mc.q = float(q)
+        mc.dq = 0.0
+        mc.tau = float(tau)
+        mc.kp = float(kp)
+        mc.kd = float(kd)
+
+    def send(self):
+        self.cmd.crc = self.crc.Crc(self.cmd)
+        self.pub.Write(self.cmd)
+
+
+def try_init_arm_sdk() -> Optional[ArmRig]:
+    try:
+        from unitree_sdk2py.core.channel import ChannelPublisher  # type: ignore
+        from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_  # type: ignore
+        from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_  # type: ignore
+        from unitree_sdk2py.utils.crc import CRC  # type: ignore
+
+        pub = ChannelPublisher("rt/arm_sdk", LowCmd_)
+        pub.Init()
+
+        cmd = unitree_hg_msg_dds__LowCmd_()
+        crc = CRC()
+
+        # enable arm_sdk mode
+        cmd.motor_cmd[29].q = 1
+        return ArmRig(pub=pub, cmd=cmd, crc=crc)
+    except Exception as e:
+        print("[arm_sdk] disabled:", e)
+        return None
+
+
+# ---------------- Core helpers ----------------
+
+def init_loco(timeout: float = 10.0) -> LocoClient:
+    bot = LocoClient()
+    bot.SetTimeout(timeout)
+    bot.Init()
+    return bot
+
+
+def init_msc(timeout: float = 5.0) -> MotionSwitcherClient:
+    msc = MotionSwitcherClient()
+    msc.SetTimeout(timeout)
+    msc.Init()
+    return msc
+
+
+def safe_call(name: str, fn: Callable[..., Any], *args, **kwargs) -> Any:
+    """
+    Unitree python wrappers often return None on success.
+    This helper prints [OK] on no-exception; prints [ERR] on exception.
+    """
+    try:
+        ret = fn(*args, **kwargs)
+        print(f"[OK] {name}: success (ret={ret})")
+        return ret
+    except Exception as e:
+        code = getattr(e, "code", None)
+        payload = getattr(e, "payload", None)
+        print(f"[ERR] {name} failed: {repr(e)} code={code} payload={payload}")
+        return None
+
+
+def stop_only(bot: Optional[LocoClient]):
+    """Stop walking/teleop velocity but do NOT change mode/posture."""
+    if bot is None:
+        return
+    safe_call("StopMove", bot.StopMove)
+
+
+def move_cmd(bot: LocoClient, vx: float, vy: float, om: float):
+    """
+    SDK variations exist. We try a safe call order.
+    """
+    try:
+        bot.Move(vx, vy, om, True)
+        return
+    except Exception:
+        pass
+
+    try:
+        bot.Move(vx, vy, om)
+        return
+    except Exception:
+        pass
+
+    try:
+        bot.Move(vx, vy, om, continuous_move=True)  # type: ignore
+        return
+    except Exception as e:
+        print("[ERR] Move failed in all signatures:", repr(e))
+
+
+def fsm_set(bot: LocoClient, fsm_id: int, label: str):
+    """
+    Prefer direct methods if present; fallback to SetFsmId(fsm_id).
+    """
+    if hasattr(bot, label):
+        fn = getattr(bot, label)
+        return safe_call(label, fn)
+
+    # common fallback in some sdk builds
+    if hasattr(bot, "SetFsmId"):
+        return safe_call(f"{label} (FSM {fsm_id}) via SetFsmId({fsm_id})", bot.SetFsmId, fsm_id)
+
+    print(f"[ERR] Neither {label} nor SetFsmId exists in this SDK build.")
+    return None
+
+
+def balance_stand(bot: LocoClient, mode: int = 0):
+    """
+    Some builds require BalanceStand(balance_mode).
+    Others expose SetBalanceMode(0/1).
+    """
+    if hasattr(bot, "BalanceStand"):
+        try:
+            return safe_call(f"BalanceStand({mode})", bot.BalanceStand, mode)
+        except TypeError:
+            return safe_call("BalanceStand()", bot.BalanceStand)
+
+    if hasattr(bot, "SetBalanceMode"):
+        return safe_call(f"SetBalanceMode({mode})", bot.SetBalanceMode, mode)
+
+    print("[WARN] No BalanceStand/SetBalanceMode available in this SDK.")
+    return None
+
+
+# ---------------- PREP / READY sequences ----------------
+
+def prep_mode(bot: LocoClient):
+    """
+    PREP = stand + balance (NO Start/FSM200).
+    Matches your tested behavior:
+      StopMove -> Damp -> StandUp(FSM4) -> ramp stand height -> BalanceStand(0) -> set final height.
+    """
+    print("[PREP] stand + balance (NO Start/FSM200) ...")
+    safe_call("StopMove", bot.StopMove)
+    safe_call("Damp", bot.Damp)
+
+    # StandUp FSM 4
+    fsm_set(bot, 4, "StandUp")
+
+    # Stand height ramp (0.02 .. 0.50)
+    if hasattr(bot, "SetStandHeight"):
+        for h in [x / 100.0 for x in range(2, 51, 2)]:
+            safe_call(f"SetStandHeight({h:.2f}m)", bot.SetStandHeight, float(h))
+            time.sleep(0.03)
+
+        # set a comfortable final height before balancing
+        safe_call("SetStandHeight(0.22m)", bot.SetStandHeight, 0.22)
+    else:
+        print("[WARN] bot.SetStandHeight not available in this SDK build.")
+
+    # Balance (static stand)
+    balance_stand(bot, mode=0)
+
+    # Re-send final height (common trick)
+    if hasattr(bot, "SetStandHeight"):
+        safe_call("SetStandHeight(0.22m)", bot.SetStandHeight, 0.22)
+
+    print("[PREP] Done. (NO Start)")
+
+
+def ready_start_mode(bot: LocoClient):
+    """
+    READY/START = PREP + Start (FSM200).
+    """
+    print("[READY] stand + balance + start (FSM 200) ...")
+    prep_mode(bot)
+    if hasattr(bot, "Start"):
+        safe_call("Start (FSM 200)", bot.Start)
+    else:
+        # some builds: SetFsmId(200)
+        fsm_set(bot, 200, "Start")
+    print("[READY] Done. (FSM200 expected)")
+
+
+# ---------------- Teleop (old keyboard function) ----------------
+
+def teleop_loop(bot: LocoClient, speed_limit: float):
+    if not HAVE_PYNPUT:
+        print("[ERR] pynput not installed. Install: pip install pynput")
+        return
+
+    LIN_STEP = 0.05
+    ANG_STEP = 0.2
+    SEND_PERIOD = 0.10  # 10 Hz
+
+    pressed = set()
+
+    def on_press(k):
+        if isinstance(k, KeyCode) and k.char:
+            pressed.add(k.char.lower())
+        else:
+            pressed.add(k)
+
+    def on_release(k):
+        if isinstance(k, KeyCode) and k.char:
+            pressed.discard(k.char.lower())
+        else:
+            pressed.discard(k)
+
+    def key(name: str) -> bool:
+        if name == "space":
+            return Key.space in pressed
+        if name == "esc":
+            return Key.esc in pressed
+        return name in pressed
+
+    def clamp(v: float) -> float:
+        return max(-speed_limit, min(speed_limit, v))
+
+    vx = vy = om = 0.0
+    last_send = 0.0
+
+    print("\n--- TELEOP ---")
+    print("Hold keys: W/S (vx), Q/E (vy), A/D (yaw), Space (stop).  ESC to exit.\n")
+
+    listener = Listener(on_press=on_press, on_release=on_release)
+    listener.start()
+
+    try:
+        while True:
+            if key("esc"):
+                stop_only(bot)
+                break
+
+            if key("w") and not key("s"):
+                vx = clamp(vx + LIN_STEP)
+            elif key("s") and not key("w"):
+                vx = clamp(vx - LIN_STEP)
+            else:
+                vx = 0.0
+
+            if key("q") and not key("e"):
+                vy = clamp(vy + LIN_STEP)
+            elif key("e") and not key("q"):
+                vy = clamp(vy - LIN_STEP)
+            else:
+                vy = 0.0
+
+            if key("a") and not key("d"):
+                om = clamp(om + ANG_STEP)
+            elif key("d") and not key("a"):
+                om = clamp(om - ANG_STEP)
+            else:
+                om = 0.0
+
+            if key("space"):
+                vx = vy = om = 0.0
+
+            now = time.time()
+            if now - last_send >= SEND_PERIOD:
+                # enable gait mode if supported (some SDKs require it for walking)
+                if hasattr(bot, "SetBalanceMode"):
+                    try:
+                        bot.SetBalanceMode(1)
+                    except Exception:
+                        pass
+
+                move_cmd(bot, vx, vy, om)
+                last_send = now
+                sys.stdout.write(f"\rvx={vx:+.2f}  vy={vy:+.2f}  yaw={om:+.2f}    ")
+                sys.stdout.flush()
+
+            time.sleep(0.005)
+
+    except KeyboardInterrupt:
+        stop_only(bot)
+        print("\n--- teleop interrupted (StopMove only) ---\n")
+    finally:
+        listener.stop()
+        print("\n--- teleop ended ---\n")
+
+
+# ---------------- Presets for arms ----------------
+
+LEFT_DOWN = [
+    (12, 0.0),
+    (15, +0.211),
+    (16, +0.181),
+    (17, -0.284),
+    (18, +0.672),
+    (19, -0.379),
+    (20, -0.852),
+    (21, -0.019),
+]
+RIGHT_DOWN = [
+    (12, 0.0),
+    (22, +0.087),
+    (23, -0.271),
+    (24, +0.323),
+    (25, +0.691),
+    (26, +0.240),
+    (27, -0.771),
+    (28, -0.176),
+]
+
+def apply_pose(arm: ArmRig, pose, kp: float = 60.0, kd: float = 1.5):
+    for j, q in pose:
+        arm.set_joint(j, q, kp=kp, kd=kd, tau=0.0)
+    arm.send()
+
+
+# ---------------- Main ----------------
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--iface", required=True, help="NIC connected to G1 (example: enp3s0)")
+    ap.add_argument("--timeout", type=float, default=10.0)
+    ap.add_argument("--topic", default="", help="LowState topic override (default tries rt/lf/lowstate then rt/lowstate)")
+    args = ap.parse_args()
+
+    # DDS init ONCE
+    ChannelFactoryInitialize(0, args.iface)
+
+    # Live monitor
+    monitor = LiveStateMonitor()
+    topics = [args.topic] if args.topic else ["rt/lf/lowstate", "rt/lowstate"]
+    sub = None
+    last_err = None
+    for t in topics:
+        try:
+            sub = ChannelSubscriber(t, LowState_)
+            sub.Init(monitor.cb, 200)
+            print(f"[OK] LowState subscribed: {t}")
+            break
+        except Exception as e:
+            last_err = e
+            print(f"[WARN] LowState subscribe failed for {t}: {e}", file=sys.stderr)
+    if sub is None:
+        print(f"[WARN] Could not subscribe LowState. Last error: {last_err}")
+
+    # Init clients
+    print("Initializing LocoClient...")
+    bot: Optional[LocoClient] = None
+    try:
+        bot = init_loco(timeout=args.timeout)
+    except Exception as e:
+        print("[ERR] Loco init failed:", e)
+
+    print("Initializing MotionSwitcherClient...")
+    msc: Optional[MotionSwitcherClient] = None
+    try:
+        msc = init_msc(timeout=5.0)
+        status, result = msc.CheckMode()
+        print(f"[MSC] Current mode: {result.get('name') or '(none)'}")
+    except Exception as e:
+        print("[WARN] MotionSwitcher init failed:", e)
+
+    arm = try_init_arm_sdk()
+
+    # Snapshot on start
+    if sub is not None and monitor.first_msg_evt.wait(timeout=2.0):
+        cnt, age, qdeg = monitor.snapshot_deg()
+        print("\n[STATE] Current joint snapshot (deg):")
+        print(f"  lowstate_msgs={cnt}  age={age:.2f}s")
+        for idx in sorted(qdeg.keys()):
+            print(f"  [{idx:02d}] {JOINT_NAMES.get(idx,'?'):<18} {qdeg[idx]:+8.2f}°")
+        print()
+
+    def need_bot() -> bool:
+        if bot is None:
+            print("[ERR] No LocoClient available.")
+            return False
+        return True
+
+    def need_arm() -> bool:
+        if arm is None:
+            print("[ERR] arm_sdk not available.")
+            return False
+        return True
+
+    # ----- Dictionary-driven menu -----
+    OPTIONS: "OrderedDict[int, Dict[str, Any]]" = OrderedDict()
+
+    OPTIONS[1] = {"label": "PREP mode (stand + balance)  [NO start/FSM200]",
+                  "fn": lambda: prep_mode(bot) if need_bot() else None}
+
+    OPTIONS[2] = {"label": "READY/START mode (stand + balance + start, FSM 200)",
+                  "fn": lambda: ready_start_mode(bot) if need_bot() else None}
+
+    OPTIONS[3] = {"label": "ZeroTorque (FSM 0)",
+                  "fn": lambda: fsm_set(bot, 0, "ZeroTorque") if need_bot() else None}
+
+    OPTIONS[4] = {"label": "Damp (FSM 1)",
+                  "fn": lambda: fsm_set(bot, 1, "Damp") if need_bot() else None}
+
+    OPTIONS[5] = {"label": "StandUp (FSM 4)",
+                  "fn": lambda: fsm_set(bot, 4, "StandUp") if need_bot() else None}
+
+    OPTIONS[6] = {"label": "Squat (FSM 2)",
+                  "fn": lambda: fsm_set(bot, 2, "Squat") if need_bot() else None}
+
+    OPTIONS[7] = {"label": "Sit (FSM 3)",
+                  "fn": lambda: fsm_set(bot, 3, "Sit") if need_bot() else None}
+
+    OPTIONS[8] = {"label": "LowStand",
+                  "fn": lambda: safe_call("LowStand", bot.LowStand) if (need_bot() and hasattr(bot, "LowStand")) else print("[ERR] LowStand not available")}
+
+    OPTIONS[9] = {"label": "HighStand",
+                  "fn": lambda: safe_call("HighStand", bot.HighStand) if (need_bot() and hasattr(bot, "HighStand")) else print("[ERR] HighStand not available")}
+
+    OPTIONS[10] = {"label": "Lie-to-Stand (FSM 702) (if supported)",
+                   "fn": lambda: fsm_set(bot, 702, "Lie2StandUp") if need_bot() else None}
+
+    OPTIONS[11] = {"label": "Walk teleop (keyboard: W/S Q/E A/D, ESC exit)",
+                   "fn": lambda: teleop_loop(bot, speed_limit=0.6) if need_bot() else None}
+
+    OPTIONS[12] = {"label": "Run teleop (keyboard: W/S Q/E A/D, ESC exit)",
+                   "fn": lambda: teleop_loop(bot, speed_limit=1.2) if need_bot() else None}
+
+    OPTIONS[16] = {"label": "Status (LIVE joints)",
+                   "fn": lambda: show_status(bot, arm, msc, sub, monitor)}
+
+    OPTIONS[17] = {"label": "MotionSwitcher: Select mode 'ai' (supported)",
+                   "fn": lambda: msc_select_ai(msc)}
+
+    OPTIONS[18] = {"label": "MotionSwitcher: Release current mode",
+                   "fn": lambda: msc_release(msc)}
+
+    OPTIONS[19] = {"label": "MotionSwitcher: Show current mode",
+                   "fn": lambda: msc_show(msc)}
+
+    OPTIONS[20] = {"label": "Seated mode ON (Sit)",
+                   "fn": lambda: fsm_set(bot, 3, "Sit") if need_bot() else None}
+
+    OPTIONS[21] = {"label": "Seated mode OFF (StandUp + PREP)",
+                   "fn": lambda: seated_off(bot) if need_bot() else None}
+
+    OPTIONS[22] = {"label": "Reconnect (re-init Loco + MotionSwitcher)",
+                   "fn": lambda: reconnect(args, lambda b: set_bot_ref(b), lambda m: set_msc_ref(m))}
+
+    # Helpers need access to outer bot/msc refs:
+    def set_bot_ref(new_bot: Optional[LocoClient]):
+        nonlocal bot
+        bot = new_bot
+
+    def set_msc_ref(new_msc: Optional[MotionSwitcherClient]):
+        nonlocal msc
+        msc = new_msc
+
+
+    def print_menu():
+        print("\n" + "=" * 70)
+        print("G1 MODE CONTROLLER - select option:")
+        print("=" * 70)
+        for k, v in OPTIONS.items():
+            print(f"{k:>3}) {v['label']}")
+        print("  0) Quit (StopMove only)")
+        print("=" * 70)
+
+    print_menu()
+
+    while True:
+        try:
+            choice = input("\nSelect option (0-22): ").strip()
+        except KeyboardInterrupt:
+            stop_only(bot)
+            print("\n[EXIT] Ctrl+C → StopMove only. Keeping current posture. Bye.")
+            break
+        except EOFError:
+            stop_only(bot)
+            print("\n[EXIT] EOF → StopMove only. Keeping current posture. Bye.")
+            break
+
+        if not choice:
+            continue
+
+        if choice == "0":
+            stop_only(bot)
+            print("\n[EXIT] Quit → StopMove only. Keeping current posture. Bye.")
+            break
+
+        try:
+            opt = int(choice)
+        except ValueError:
+            print("Please enter a number.")
+            continue
+
+        if opt not in OPTIONS:
+            print("Unknown option.")
+            print_menu()
+            continue
+
+        try:
+            OPTIONS[opt]["fn"]()
+        except KeyboardInterrupt:
+            stop_only(bot)
+            print("\n[INTERRUPT] Ctrl+C → StopMove only. Keeping current posture.")
+        except Exception as e:
+            print("[ERR] Action failed:", repr(e))
+
+        print_menu()
+
+
+# --------- extra action helpers that need current refs ---------
+
+def show_status(bot, arm, msc, sub, monitor):
+    print("Status:")
+    print(f"  LocoClient:      {'OK' if bot is not None else 'NOT INITIALISED'}")
+    print(f"  MotionSwitcher:  {'OK' if msc is not None else 'DISABLED'}")
+    if msc is not None:
+        try:
+            _s, r = msc.CheckMode()
+            print(f"  MSC mode:        {r.get('name') or '(none)'}")
+        except Exception:
+            print("  MSC mode:        (error reading)")
+    print(f"  arm_sdk:         {'OK' if arm is not None else 'DISABLED'}")
+    print(f"  pynput:          {'OK' if HAVE_PYNPUT else 'NOT INSTALLED'}")
+
+    if sub is None:
+        print("  LowState:        NOT SUBSCRIBED")
+        return
+
+    cnt, age, qdeg = monitor.snapshot_deg()
+    print(f"  LowState:        OK  msgs={cnt}  age={age:.2f}s")
+    print("  Current joints (deg):")
+    for idx in sorted(qdeg.keys()):
+        print(f"    [{idx:02d}] {JOINT_NAMES.get(idx,'?'):<18} {qdeg[idx]:+8.2f}°")
+
+
+def msc_select_ai(msc: Optional[MotionSwitcherClient]):
+    if msc is None:
+        print("[ERR] MotionSwitcher not available.")
+        return
+    ret = safe_call("SelectMode('ai')", msc.SelectMode, "ai")
+    # ret could be (7004,None) etc; we just print it from safe_call.
+
+
+def msc_release(msc: Optional[MotionSwitcherClient]):
+    if msc is None:
+        print("[ERR] MotionSwitcher not available.")
+        return
+    safe_call("ReleaseMode()", msc.ReleaseMode)
+
+
+def msc_show(msc: Optional[MotionSwitcherClient]):
+    if msc is None:
+        print("[ERR] MotionSwitcher not available.")
+        return
+    try:
+        s, r = msc.CheckMode()
+        print(f"[MSC] Current mode: {r.get('name') or '(none)'}")
+    except Exception as e:
+        print("[ERR] CheckMode failed:", repr(e))
+
+
+def seated_off(bot: LocoClient):
+    print("[SEATED] Leaving seated: StandUp + PREP (no Start)...")
+    fsm_set(bot, 4, "StandUp")
+    prep_mode(bot)
+
+
+def reconnect(args, set_bot, set_msc):
+    print("Reconnecting LocoClient...")
+    try:
+        b = init_loco(timeout=args.timeout)
+        set_bot(b)
+        print("[OK] LocoClient reconnected.")
+    except Exception as e:
+        print("[ERR] Loco reconnect failed:", repr(e))
+        set_bot(None)
+
+    print("Reconnecting MotionSwitcherClient...")
+    try:
+        m = init_msc(timeout=5.0)
+        set_msc(m)
+        try:
+            _s, r = m.CheckMode()
+            print(f"[MSC] Current mode: {r.get('name') or '(none)'}")
+        except Exception:
+            pass
+        print("[OK] MotionSwitcher reconnected.")
+    except Exception as e:
+        print("[ERR] MotionSwitcher reconnect failed:", repr(e))
+        set_msc(None)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Controller/g1_replay_trigger_r2x.py b/Controller/g1_replay_trigger_r2x.py
new file mode 100644
index 0000000..fb3468a
--- /dev/null
+++ b/Controller/g1_replay_trigger_r2x.py
@@ -0,0 +1,381 @@
+#!/usr/bin/env python3
+"""
+G1 REPLAY TRIGGER (R2 + X) - SINGLE FILE
+---------------------------------------
+- Hold R2 + X to replay DataG1/photo_G3.jsonl (default).
+- While playing: ignore any additional R2+X until replay finishes and returns home.
+- Cancel combo: R2 + L1 cancels replay immediately and returns arms to home.
+
+Usage:
+  python3 g1_replay_trigger_r2x.py enp3s0
+  python3 g1_replay_trigger_r2x.py enp3s0 --input photo_G3.jsonl --home arm_home.jsonl --speed 1.0
+"""
+
+import time
+import sys
+import json
+import argparse
+import struct
+from pathlib import Path
+
+from unitree_sdk2py.core.channel import ChannelPublisher, ChannelSubscriber, ChannelFactoryInitialize
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowState_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowCmd_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_
+from unitree_sdk2py.utils.crc import CRC
+
+from Logger import Logs
+
+# ---------------- Logger (exactly as requested) ----------------
+controller_logs = Logs()
+controller_logs.LogEngine("G1_Logs", "g1_replay_trigger_r2x.log")
+
+# ---------------- Constants ----------------
+G1_NUM_MOTOR = 29
+ENABLE_ARM_SDK_INDEX = 29
+
+DATA_DIR = Path("DataG1")
+REPLAY_HZ = 60.0
+
+# --- GAINS (same as your replay) ---
+KP_HIGH = 300.0  # Core/Legs
+KD_HIGH = 3.0
+KP_LOW  = 80.0   # Arms/Ankles
+KD_LOW  = 3.0
+KP_WRIST = 40.0
+KD_WRIST = 1.5
+
+WEAK_MOTORS  = [4, 10, 15, 16, 17, 18, 22, 23, 24, 25]
+WRIST_MOTORS = [19, 20, 21, 26, 27, 28]
+
+# ---------------- Helpers ----------------
+def resolve_input_path(in_path: str) -> str:
+    p = Path(in_path)
+    if len(p.parts) == 1:
+        return str(DATA_DIR / p.name)
+    return str(p)
+
+def load_home_pose(home_path: str):
+    """Reads the last frame of arm_home.jsonl to get the target pose."""
+    path = resolve_input_path(home_path)
+    try:
+        last_valid_q = None
+        with open(path, 'r') as f:
+            for line in f:
+                d = json.loads(line)
+                if 'q' in d and len(d['q']) == G1_NUM_MOTOR:
+                    last_valid_q = d['q']
+        if last_valid_q:
+            controller_logs.print_and_log(f"✅ Loaded Home Pose from {path}", "info")
+            return last_valid_q
+        else:
+            controller_logs.print_and_log(f"⚠️  Warning: {path} found but contained no valid 'q' data.", "warning")
+    except FileNotFoundError:
+        controller_logs.print_and_log(f"⚠️  Warning: Home file {path} not found.", "warning")
+
+    controller_logs.print_and_log("⚠️  Using Default Home (Arms at 0.0)", "warning")
+    return [0.0] * G1_NUM_MOTOR
+
+# ---------------- Wireless Controller Parser (embedded) ----------------
+class unitreeRemoteController:
+    def __init__(self):
+        self.Lx = 0; self.Rx = 0; self.Ry = 0; self.Ly = 0
+        self.L1 = 0; self.L2 = 0; self.R1 = 0; self.R2 = 0
+        self.A = 0; self.B = 0; self.X = 0; self.Y = 0
+        self.Up = 0; self.Down = 0; self.Left = 0; self.Right = 0
+        self.Select = 0; self.F1 = 0; self.F3 = 0; self.Start = 0
+
+    def parse_botton(self, data1, data2):
+        self.R1 = (data1 >> 0) & 1; self.L1 = (data1 >> 1) & 1
+        self.Start = (data1 >> 2) & 1; self.Select = (data1 >> 3) & 1
+        self.R2 = (data1 >> 4) & 1; self.L2 = (data1 >> 5) & 1
+        self.F1 = (data1 >> 6) & 1; self.F3 = (data1 >> 7) & 1
+        self.A = (data2 >> 0) & 1; self.B = (data2 >> 1) & 1
+        self.X = (data2 >> 2) & 1; self.Y = (data2 >> 3) & 1
+        self.Up = (data2 >> 4) & 1; self.Right = (data2 >> 5) & 1
+        self.Down = (data2 >> 6) & 1; self.Left = (data2 >> 7) & 1
+
+    def parse_key(self, data):
+        offsets = [4, 8, 12, 20]  # Lx, Rx, Ry, Ly
+        self.Lx, self.Rx, self.Ry, self.Ly = [struct.unpack('<f', data[o:o+4])[0] for o in offsets]
+
+    def parse(self, remoteData):
+        self.parse_key(remoteData)
+        self.parse_botton(remoteData[2], remoteData[3])
+
+    def get_state(self):
+        return self.__dict__.copy()
+
+# ---------------- Replay Engine ----------------
+class ReplayWithHome:
+    def __init__(self, watchdog_timeout=0.25, watchdog_disable_after=1.0):
+        self.low_state = None
+        self.low_cmd = unitree_hg_msg_dds__LowCmd_()
+        self.crc = CRC()
+
+        self.arm_pub = ChannelPublisher("rt/arm_sdk", LowCmd_)
+        self.arm_pub.Init()
+
+        self.state_sub = None
+        self.first_state = False
+
+        # watchdog
+        self.last_state_time = 0.0
+        self.watchdog_timeout = float(watchdog_timeout)
+        self.watchdog_disable_after = float(watchdog_disable_after)
+
+        # controller
+        self.remote = unitreeRemoteController()
+        self.controller_state = self.remote.get_state()
+
+        self.is_playing = False
+
+    def InitStateSubscriber(self, topic: str):
+        self.state_sub = ChannelSubscriber(topic, LowState_)
+        self.state_sub.Init(self.LowStateHandler, 10)
+
+    def LowStateHandler(self, msg: LowState_):
+        self.low_state = msg
+        self.first_state = True
+        self.last_state_time = time.time()
+        try:
+            self.remote.parse(msg.wireless_remote)
+            self.controller_state = self.remote.get_state()
+        except Exception:
+            pass
+
+    def StateFresh(self) -> bool:
+        return (time.time() - self.last_state_time) < self.watchdog_timeout
+
+    def CancelRequested(self) -> bool:
+        """Cancel combo: R2 + L1"""
+        s = self.controller_state
+        return bool(s.get("R2", 0) and s.get("L1", 0))
+
+    def SendFrame(self, arm_target_q, body_lock_q):
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 1.0
+
+        for i in range(G1_NUM_MOTOR):
+            self.low_cmd.motor_cmd[i].mode = 1
+            self.low_cmd.motor_cmd[i].dq = 0
+            self.low_cmd.motor_cmd[i].tau = 0
+
+            if i >= 15:
+                self.low_cmd.motor_cmd[i].q = arm_target_q[i]
+            else:
+                self.low_cmd.motor_cmd[i].q = body_lock_q[i]
+
+            if i in WEAK_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_LOW
+                self.low_cmd.motor_cmd[i].kd = KD_LOW
+            elif i in WRIST_MOTORS:
+                self.low_cmd.motor_cmd[i].kp = KP_WRIST
+                self.low_cmd.motor_cmd[i].kd = KD_WRIST
+            else:
+                self.low_cmd.motor_cmd[i].kp = KP_HIGH
+                self.low_cmd.motor_cmd[i].kd = KD_HIGH
+
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        self.arm_pub.Write(self.low_cmd)
+
+    def DisableSDK(self):
+        controller_logs.print_and_log("🔌 Disabling SDK...", "info")
+        self.low_cmd.motor_cmd[ENABLE_ARM_SDK_INDEX].q = 0.0
+        self.low_cmd.crc = self.crc.Crc(self.low_cmd)
+        for _ in range(10):
+            self.arm_pub.Write(self.low_cmd)
+            time.sleep(0.02)
+
+    def ReturnArmsHome(self, last_arm_q, body_lock_q, home_q, home_steps=180):
+        """Always go home smoothly (arms only), body remains locked."""
+        controller_logs.print_and_log("🏡 Returning arms to HOME...", "info")
+        for k in range(home_steps):
+            stale_for = (time.time() - self.last_state_time)
+            if stale_for > self.watchdog_disable_after:
+                controller_logs.print_and_log("🛑 WATCHDOG: connection lost during home. Disabling SDK.", "error")
+                self.DisableSDK()
+                return
+
+            if not self.StateFresh():
+                controller_logs.print_and_log("⚠️ WATCHDOG: state stale during home. Holding last pose...", "warning")
+                self.SendFrame(last_arm_q, body_lock_q)
+                time.sleep(1.0 / REPLAY_HZ)
+                continue
+
+            alpha = k / home_steps
+            interp_q = list(last_arm_q)
+            for j in range(15, 29):
+                interp_q[j] = (1-alpha)*last_arm_q[j] + alpha*home_q[j]
+            self.SendFrame(interp_q, body_lock_q)
+            time.sleep(1.0 / REPLAY_HZ)
+
+        controller_logs.print_and_log("✅ Home Reached.", "info")
+
+    def RunReplay(self, filename: str, home_filename: str, speed: float):
+        self.is_playing = True
+        cancel_requested = False
+
+        try:
+            controller_logs.print_and_log(f"🎬 Triggered replay: {filename}", "info")
+
+            controller_logs.print_and_log("Waiting for robot...", "info")
+            while not self.first_state:
+                time.sleep(0.05)
+            controller_logs.print_and_log("✅ Robot Connected!", "info")
+
+            home_q = load_home_pose(home_filename)
+            full_body_lock_q = [self.low_state.motor_state[i].q for i in range(G1_NUM_MOTOR)]
+
+            frames = []
+            with open(filename, 'r') as f:
+                for line in f:
+                    d = json.loads(line)
+                    if 'q' in d:
+                        frames.append(d)
+
+            if not frames:
+                controller_logs.print_and_log("❌ No frames found in input file.", "error")
+                return
+
+            controller_logs.print_and_log(f"🟢 Ready to play {len(frames)} frames.", "info")
+            controller_logs.print_and_log("🔒 Body LOCKED. Arms replay then return HOME.", "info")
+            controller_logs.print_and_log("🛑 Cancel combo: Hold R2 + L1", "info")
+
+            # Move to start
+            controller_logs.print_and_log("Moving to start...", "info")
+            file_start_q = frames[0]['q']
+            last_played_q = file_start_q  # will update
+
+            steps = 60
+            for k in range(steps):
+                if self.CancelRequested():
+                    controller_logs.print_and_log("🛑 CANCEL: R2+L1 detected أثناء الانتقال للبداية.", "warning")
+                    cancel_requested = True
+                    break
+
+                if not self.StateFresh():
+                    controller_logs.print_and_log("⚠️ WATCHDOG: stale while moving to start. Holding...", "warning")
+                    self.SendFrame(last_played_q, full_body_lock_q)
+                    time.sleep(1.0 / REPLAY_HZ)
+                    continue
+
+                alpha = k / steps
+                interp_q = list(full_body_lock_q)
+                for j in range(15, 29):
+                    interp_q[j] = (1-alpha)*full_body_lock_q[j] + alpha*file_start_q[j]
+                self.SendFrame(interp_q, full_body_lock_q)
+                last_played_q = interp_q
+                time.sleep(1.0 / REPLAY_HZ)
+
+            # Play replay (unless canceled)
+            if not cancel_requested:
+                controller_logs.print_and_log("▶️ Playing...", "info")
+                play_elapsed = 0.0
+                last_real = time.time()
+
+                while True:
+                    if self.CancelRequested():
+                        controller_logs.print_and_log("🛑 CANCEL: R2+L1 detected أثناء التشغيل.", "warning")
+                        cancel_requested = True
+                        break
+
+                    stale_for = (time.time() - self.last_state_time)
+                    if stale_for > self.watchdog_disable_after:
+                        controller_logs.print_and_log("🛑 WATCHDOG: connection lost. Disabling SDK.", "error")
+                        self.DisableSDK()
+                        return
+
+                    if not self.StateFresh():
+                        controller_logs.print_and_log("⚠️ WATCHDOG: timeout! Holding last pose...", "warning")
+                        self.SendFrame(last_played_q, full_body_lock_q)
+                        time.sleep(1.0 / REPLAY_HZ)
+                        continue
+
+                    now_real = time.time()
+                    dt_real = now_real - last_real
+                    last_real = now_real
+                    play_elapsed += dt_real * speed
+
+                    target_frame = None
+                    for fr in frames:
+                        if fr['t'] - frames[0]['t'] >= play_elapsed:
+                            target_frame = fr
+                            break
+                    if target_frame is None:
+                        break
+
+                    self.SendFrame(target_frame['q'], full_body_lock_q)
+                    last_played_q = target_frame['q']
+                    time.sleep(1.0 / REPLAY_HZ)
+
+            # Always go home (normal finish OR cancel)
+            self.ReturnArmsHome(last_played_q, full_body_lock_q, home_q, home_steps=180)
+            self.DisableSDK()
+
+        except Exception as e:
+            controller_logs.print_and_log(f"❌ Exception in replay: {e}", "error")
+            try:
+                self.DisableSDK()
+            except Exception:
+                pass
+        finally:
+            self.is_playing = False
+
+# ---------------- Trigger Loop (R2 + X) ----------------
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("iface", help="Network interface")
+    parser.add_argument("--input", default="photo_G3.jsonl", help="Input recording file (default: photo_G3.jsonl)")
+    parser.add_argument("--home", default="arm_home.jsonl", help="Home pose file")
+    parser.add_argument("--speed", type=float, default=1.0)
+    parser.add_argument("--state_topic", default="rt/lowstate", help="Lowstate topic (default: rt/lowstate)")
+    parser.add_argument("--watchdog", type=float, default=0.25, help="Watchdog timeout seconds")
+    parser.add_argument("--watchdog_disable_after", type=float, default=1.0, help="Disable SDK if stale longer than this")
+    args = parser.parse_args()
+
+    ChannelFactoryInitialize(0, args.iface)
+
+    engine = ReplayWithHome(
+        watchdog_timeout=args.watchdog,
+        watchdog_disable_after=args.watchdog_disable_after
+    )
+    engine.InitStateSubscriber(args.state_topic)
+
+    target_path = resolve_input_path(args.input)
+
+    controller_logs.print_and_log("✅ G1 R2+X Trigger Ready", "info")
+    controller_logs.print_and_log(f"🎯 Trigger file: {target_path}", "info")
+    controller_logs.print_and_log("🎮 Hold R2 + X to replay", "info")
+    controller_logs.print_and_log("🛑 Cancel while playing: Hold R2 + L1", "info")
+
+    # Rising-edge trigger + require release before next trigger
+    combo_prev = False
+
+    while True:
+        time.sleep(0.01)
+
+        if not engine.first_state:
+            continue
+
+        s = engine.controller_state
+
+        # Trigger combo: R2 + X
+        combo_now = bool(s.get("R2", 0) and s.get("X", 0))
+
+        # ✅ IGNORE while playing (and also prevent retrigger until released)
+        if engine.is_playing:
+            combo_prev = combo_now
+            continue
+
+        # Trigger only on rising edge (must release and press again)
+        if combo_now and not combo_prev:
+            controller_logs.print_and_log("[TRIGGER] R2 + X detected", "info")
+            engine.RunReplay(target_path, args.home, args.speed)
+            # After RunReplay returns, if user still holding, combo_prev will get updated next loop
+            # and won’t retrigger until they release and press again.
+
+        combo_prev = combo_now
+
+if __name__ == "__main__":
+    main()
diff --git a/Controller/hanger_boot_sequence.py b/Controller/hanger_boot_sequence.py
new file mode 100644
index 0000000..9c14d59
--- /dev/null
+++ b/Controller/hanger_boot_sequence.py
@@ -0,0 +1,185 @@
+"""
+hanger_boot_sequence.py – utility to bring a hanging Unitree G-1 from
+power-on (Damp) to a balanced stand ready for walking.  The helper will now
+detect when the robot is already in that balanced stand (FSM-200) and simply
+return the client immediately, avoiding a redundant second bring-up cycle.
+
+Call `hanger_boot_sequence()` from any script and it returns an initialised
+`LocoClient` instance that is already in FSM-200.  The helper now performs a
+sanity-check after the leg-extension sweep: if the firmware still reports
+“feet unloaded” (mode = 2) we pause, print a warning and wait for the
+operator to tweak the hanger height and press <Enter>.  The sweep is then
+repeated until mode 0 (feet loaded) is observed.  All parameters are
+optional and identical to those we used during development.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Optional
+
+from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+from unitree_sdk2py.g1.loco.g1_loco_client import LocoClient
+from unitree_sdk2py.g1.loco.g1_loco_api import (
+    ROBOT_API_ID_LOCO_GET_FSM_ID,
+    ROBOT_API_ID_LOCO_GET_FSM_MODE,
+)
+
+
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+
+
+def _rpc_get_int(client: LocoClient, api_id: int) -> Optional[int]:
+    try:
+        code, data = client._Call(api_id, "{}")  # type: ignore[attr-defined]
+        if code == 0 and data:
+            import json
+
+            return json.loads(data).get("data")
+    except Exception:
+        pass
+    return None
+
+
+def _fsm_id(client: LocoClient) -> Optional[int]:
+    return _rpc_get_int(client, ROBOT_API_ID_LOCO_GET_FSM_ID)
+
+
+def _fsm_mode(client: LocoClient) -> Optional[int]:
+    return _rpc_get_int(client, ROBOT_API_ID_LOCO_GET_FSM_MODE)
+
+
+def hanger_boot_sequence(
+    iface: str = "enp68s0f1",
+    step: float = 0.02,
+    max_height: float = 0.5,
+    logger: Optional[logging.Logger] = None,
+) -> LocoClient:
+    """Run the hanger-to-stand sequence.
+
+    Returns a LocoClient instance that is in FSM-200 and ready to receive
+    Move / Velocity commands.
+    """
+
+    if logger is None:
+        logging.basicConfig(level=logging.INFO, format="%(message)s")
+        logger = logging.getLogger("hanger_boot")
+
+    # DDS initialisation ---------------------------------------------------
+    ChannelFactoryInitialize(0, iface)
+
+    bot = LocoClient()
+    bot.SetTimeout(10.0)
+    bot.Init()
+
+    # ------------------------------------------------------------------
+    # Early-out: If the robot is already in a balanced stand (feet loaded
+    # and balance controller running) there is no need to repeat the whole
+    # hanger bring-up sequence.  Re-running it would at best waste time and
+    # at worst jolt a robot that is happily standing on the ground.
+    #
+    # Our working definition of “balanced stand ready for walking” is:
+    #     • FSM-ID 200  (Start – balance controller engaged)
+    #     • SportModeState.mode != 2  (feet *loaded*)
+    #
+    # When this condition is met we simply log the situation and return the
+    # initialised LocoClient so callers can proceed to send velocity
+    # commands straight away.
+    # ------------------------------------------------------------------
+
+    try:
+        cur_id = _fsm_id(bot)
+        cur_mode = _fsm_mode(bot)
+
+        if cur_id == 200 and cur_mode is not None and cur_mode != 2:
+            logger.info(
+                "Robot already in balanced stand (FSM 200, mode %s) – skipping boot sequence.",
+                cur_mode,
+            )
+
+            # Leave the existing balance mode unchanged – if the operator
+            # previously enabled continuous gait it will remain active; if
+            # the robot was static it will stay still.  This avoids toggling
+            # modes unnecessarily and prevents inadvertent “stepping in
+            # place” when no motion is desired.
+
+            return bot
+    except Exception:
+        # Fallback to the full sequence if any check fails (e.g. communication
+        # hiccup right after power-up).  Better to run the safe, proven
+        # routine than to guess incorrectly.
+        pass
+
+    def show(tag: str) -> None:
+        logger.info("%-12s → FSM %s   mode %s", tag, _fsm_id(bot), _fsm_mode(bot))
+
+    # 1. Damp --------------------------------------------------------------
+    bot.Damp(); show("damp")
+
+    # 2. Stand-up ----------------------------------------------------------
+    bot.SetFsmId(4); show("stand_up")  # stand-up helper missing in wrapper
+
+    # ------------------------------------------------------------------
+    # 3. Increment stand-height until the firmware reports that the feet
+    #    are *loaded* (SportModeState.mode == 0).  If we reach the maximum
+    #    extension without seeing the transition 2 -> 0 it usually means
+    #    the hanging frame is too high or too low and the soles never make
+    #    solid contact with the ground.  In that case we pause, prompt the
+    #    user to adjust the hanger height, then try the extension sweep
+    #    again.
+    # ------------------------------------------------------------------
+
+    while True:  # retry loop – exits as soon as mode-0 (feet loaded) seen
+        height = 0.0
+
+        while height < max_height:
+            height += step
+            bot.SetStandHeight(height)
+            show(f"height {height:.2f} m")
+
+            # Break early once the firmware acknowledges feet contact
+            if _fsm_mode(bot) == 0 and height > 0.2:
+                break
+
+        # Success condition – mode 0 means the robot is now supporting its
+        # weight on the ground and we can continue to balance stand.
+        if _fsm_mode(bot) == 0:
+            break
+
+        # Otherwise we failed to load the feet.  Tell the user and let them
+        # tweak the hanging frame before we repeat the sweep.
+        logger.warning(
+            "Feet still unloaded (mode %s) after reaching %.2f m.\n"
+            "Adjust hanger height (raise/lower until the soles are just in\n"
+            "contact with the ground) then press <Enter> to try again…",
+            _fsm_mode(bot),
+            height,
+        )
+
+        try:
+            # Reduce stand height so the operator can reposition safely.
+            bot.SetStandHeight(0.0)
+            show("reset")
+        except Exception:
+            pass
+
+        input()  # wait for operator acknowledgement
+
+    # 4. Balance stand -----------------------------------------------------
+    bot.BalanceStand(0); show("balance")
+    bot.SetStandHeight(height); show("height✔")
+
+    # 5. Start the balance controller (FSM 200) ---------------------------
+    # Leave the robot in balance-mode 0 (static) – callers can switch to
+    # continuous gait (balance-mode 1) when they actually want to walk.
+
+    bot.Start(); show("start")
+
+    # Caller can now send velocity commands.
+    return bot
+
+
+__all__ = ["hanger_boot_sequence"]
diff --git a/Controller/keyboard_controller.py b/Controller/keyboard_controller.py
new file mode 100644
index 0000000..eff694f
--- /dev/null
+++ b/Controller/keyboard_controller.py
@@ -0,0 +1,208 @@
+#!/usr/bin/env python3
+"""
+keyboard_controller.py – simple WASD-style tele-op for Unitree G-1.
+
+The script:
+1. Runs the hanger boot sequence so the robot starts balancing.
+2. Enters a curses UI where you can drive with the keys below.
+
+Controls
+---------
+    W / S : forward / backward velocity
+    A / D : yaw left / right (turn)
+    Q / E : lateral left / right (optional, G-1 supports side-step)
+    Space : stop (zero velocities)
+    Z      : Damp (soft) and exit
+    Esc    : emergency stop & exit (ZeroTorque)
+
+Velocities are applied continuously – every key-press adjusts the target
+values which are sent to the robot at 10 Hz.
+"""
+
+from __future__ import annotations
+
+import argparse
+
+# ------------------------------------------------------------------------
+# Notes on the 2025-04-28 update
+# ------------------------------------------------------------------------
+#
+# * Continuous command _while a key is physically held_. As soon as the key is
+#   released the corresponding velocity is reset to **zero**.
+# * Supports holding several keys together – e.g. **W + A** to move forward
+#   while turning left.
+#
+# This requires real “key-up” events which the `curses` module cannot provide.
+# We now use the lightweight third-party `pynput` package to poll the current
+# key state.  It communicates with the X-server (Linux), Win32, or Quartz and
+# therefore works for normal users on typical desktop sessions (no sudo).
+# Curses is kept only for drawing the tiny on-screen HUD.
+#
+# When running under Wayland `pynput` might still fall back to reading
+# `/dev/input/event*`; in that corner-case you’d again need the permissions or
+# group/udev tweaks previously mentioned.
+# ------------------------------------------------------------------------
+
+import time
+
+# Third-party ---------------------------------------------------------------
+
+import curses
+
+# We now use the cross-platform `pynput` library which reads key events via the
+# X server / Win32 API / Quartz, so it works unprivileged on desktop Linux,
+# macOS and Windows.  On Wayland sessions `pynput` falls back to /dev/input and
+# may again need the permissions discussed earlier—but on X11 (the default on
+# many distros) it works out-of-the-box.
+
+try:
+    from pynput.keyboard import Listener, Key, KeyCode  # type: ignore
+except ModuleNotFoundError as exc:  # pragma: no cover
+    raise SystemExit(
+        "The 'pynput' package is required for keyboard_controller.py.\n"
+        "Install with:  pip install pynput"
+    ) from exc
+
+from hanger_boot_sequence import hanger_boot_sequence
+
+
+# ---------------------------------------------------------------------------
+# Parameter defaults
+# ---------------------------------------------------------------------------
+
+LIN_STEP = 0.05  # m/s per press
+ANG_STEP = 0.2   # rad/s per press
+
+SEND_PERIOD = 0.1  # seconds (10 Hz)
+
+
+def clamp(value: float, limit: float = 0.6) -> float:
+    return max(-limit, min(limit, value))
+
+
+def drive_loop(stdscr: "curses._CursesWindow", bot) -> None:
+    # Curses setup for a tiny on–screen HUD.
+    curses.cbreak()
+    stdscr.nodelay(True)  # Make getch() non-blocking so the UI stays alive.
+
+    # ------------------------------------------------------------------
+    # Internal state
+    # ------------------------------------------------------------------
+    vx = vy = omega = 0.0  # target velocities that will be sent to the robot
+
+    last_send = 0.0
+
+    # ------------------------------------------------------------------
+    # Keyboard listener setup (pynput)
+    # ------------------------------------------------------------------
+
+    pressed_keys: set[object] = set()  # holds Key / single-char strings
+
+    def _on_press(key):  # noqa: D401 – tiny helper
+        """Callback – store the key object / char in *pressed_keys*."""
+        if isinstance(key, KeyCode) and key.char is not None:
+            pressed_keys.add(key.char.lower())
+        else:
+            pressed_keys.add(key)
+
+    def _on_release(key):
+        if isinstance(key, KeyCode) and key.char is not None:
+            pressed_keys.discard(key.char.lower())
+        else:
+            pressed_keys.discard(key)
+
+    listener = Listener(on_press=_on_press, on_release=_on_release)
+    listener.start()
+
+    def key(name: str) -> bool:  # helper similar to keyboard.is_pressed
+        if name == "space":
+            return Key.space in pressed_keys
+        if name == "esc":
+            return Key.esc in pressed_keys
+        return name in pressed_keys
+
+    try:
+        while True:
+            # ------------------------------------------------------------------
+            # 1.  Build/refresh the target velocity based on current key states.
+            # ------------------------------------------------------------------
+
+            if key("w") and not key("s"):
+                vx = clamp(vx + LIN_STEP)
+            elif key("s") and not key("w"):
+                vx = clamp(vx - LIN_STEP)
+            else:
+                vx = 0.0
+
+            if key("q") and not key("e"):
+                vy = clamp(vy + LIN_STEP)
+            elif key("e") and not key("q"):
+                vy = clamp(vy - LIN_STEP)
+            else:
+                vy = 0.0
+
+            if key("a") and not key("d"):
+                omega = clamp(omega + ANG_STEP)
+            elif key("d") and not key("a"):
+                omega = clamp(omega - ANG_STEP)
+            else:
+                omega = 0.0
+
+            # Space bar – an emergency stop of sorts that zeroes everything no
+            # matter what other keys are held.
+            if key("space"):
+                vx = vy = omega = 0.0
+
+            # ------------------------------------------------------------------
+            # 2.  Handle exit conditions.
+            # ------------------------------------------------------------------
+            if key("z"):
+                bot.Damp()
+                break
+
+            if key("esc"):
+                bot.StopMove()
+                bot.ZeroTorque()
+                break
+
+            # ------------------------------------------------------------------
+            # 3.  Send the command at the configured rate and update HUD.
+            # ------------------------------------------------------------------
+            now = time.time()
+            if now - last_send >= SEND_PERIOD:
+                bot.Move(vx, vy, omega, continous_move=True)
+                last_send = now
+
+                stdscr.erase()
+                stdscr.addstr(0, 0, "Hold keys to drive – Z: quit  ESC: e-stop")
+                stdscr.addstr(2, 0, f"vx: {vx:+.2f}  vy: {vy:+.2f}  omega: {omega:+.2f}")
+                stdscr.refresh()
+
+            # A very small sleep keeps CPU usage civilised (<1 % on typical PCs).
+            time.sleep(0.005)
+
+    finally:
+        # Ensure the listener thread is stopped before leaving curses context.
+        listener.stop()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--iface", default="enp3s0", help="network interface connected to robot")
+    args = parser.parse_args()
+
+    # Boot sequence – returns initialised LocoClient in FSM-200
+    bot = hanger_boot_sequence(iface=args.iface)
+
+    curses.wrapper(drive_loop, bot)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nInterrupted – sending Damp …")
+        try:
+            bot.Damp()  # type: ignore[name-defined]
+        except Exception:
+            pass
diff --git a/Controller/saqr_g1_bridge.py b/Controller/saqr_g1_bridge.py
new file mode 100644
index 0000000..ea2f8a3
--- /dev/null
+++ b/Controller/saqr_g1_bridge.py
@@ -0,0 +1,265 @@
+#!/usr/bin/env python3
+"""
+saqr_g1_bridge.py
+
+Bridge between Saqr PPE detection and the Unitree G1 arm action client.
+
+Spawns Saqr (Project/Saqr/saqr.py) as a subprocess, parses its event stream,
+and triggers the G1 'reject' arm action (id=13) whenever a tracked person
+transitions to UNSAFE (= DANGER). SAFE and PARTIAL never trigger an action.
+
+Saqr event line format (from emit_event in saqr.py):
+    ID 0001 | NEW | UNSAFE | wearing: ... | missing: ... | unknown: ...
+    ID 0001 | STATUS_CHANGE | SAFE  | wearing: ... | missing: ... | unknown: ...
+
+Usage:
+    # default: webcam, default DDS interface
+    python3 saqr_g1_bridge.py
+
+    # specify camera and DDS interface
+    python3 saqr_g1_bridge.py --source 0 --iface enp3s0
+
+    # dry run (no robot movement, just print decisions)
+    python3 saqr_g1_bridge.py --dry-run
+
+    # forward extra args to saqr.py after a `--`
+    python3 saqr_g1_bridge.py --iface eth0 -- --conf 0.4 --imgsz 640
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+import signal
+import subprocess
+import sys
+import threading
+import time
+from pathlib import Path
+from typing import Dict, Optional
+
+
+# ── Defaults ─────────────────────────────────────────────────────────────────
+HERE         = Path(__file__).resolve().parent
+REPO_ROOT    = HERE.parent.parent                    # .../yslootahtech
+SAQR_DIR     = REPO_ROOT / "Project" / "Saqr"
+SAQR_SCRIPT  = SAQR_DIR / "saqr.py"
+
+DANGER_STATUS = "UNSAFE"
+REJECT_ACTION = "reject"
+RELEASE_ACTION = "release arm"
+
+# ID NNNN | EVENT_TYPE | STATUS | wearing: ... | missing: ... | unknown: ...
+EVENT_RE = re.compile(
+    r"^ID\s+(?P<id>\d+)\s*\|\s*"
+    r"(?P<event>NEW|STATUS_CHANGE)\s*\|\s*"
+    r"(?P<status>SAFE|PARTIAL|UNSAFE)\s*\|"
+)
+
+
+# ── G1 arm controller (lazy import: SDK only loaded when not in dry-run) ─────
+class ArmController:
+    def __init__(self, iface: Optional[str], timeout: float, dry_run: bool):
+        self.dry_run = dry_run
+        if dry_run:
+            print("[BRIDGE] DRY RUN — G1 SDK will not be loaded.", flush=True)
+            self.client = None
+            return
+
+        from unitree_sdk2py.core.channel import ChannelFactoryInitialize
+        from unitree_sdk2py.g1.arm.g1_arm_action_client import (
+            G1ArmActionClient,
+            action_map,
+        )
+        self._action_map = action_map
+
+        if iface:
+            ChannelFactoryInitialize(0, iface)
+        else:
+            ChannelFactoryInitialize(0)
+
+        self.client = G1ArmActionClient()
+        self.client.SetTimeout(timeout)
+        self.client.Init()
+        print(f"[BRIDGE] G1ArmActionClient ready (iface={iface or 'default'})",
+              flush=True)
+
+    def reject(self, release_after: float):
+        if self.dry_run:
+            print(f"[BRIDGE] (dry) would run '{REJECT_ACTION}' "
+                  f"then release after {release_after:.1f}s", flush=True)
+            return
+        if REJECT_ACTION not in self._action_map:
+            print(f"[BRIDGE][ERR] '{REJECT_ACTION}' not in SDK action_map",
+                  flush=True)
+            return
+        print(f"[BRIDGE] -> {REJECT_ACTION}", flush=True)
+        self.client.ExecuteAction(self._action_map[REJECT_ACTION])
+        if release_after > 0:
+            time.sleep(release_after)
+            print(f"[BRIDGE] -> {RELEASE_ACTION}", flush=True)
+            self.client.ExecuteAction(self._action_map[RELEASE_ACTION])
+
+
+# ── Bridge ───────────────────────────────────────────────────────────────────
+class Bridge:
+    def __init__(
+        self,
+        arm: ArmController,
+        cooldown_s: float,
+        release_after_s: float,
+    ):
+        self.arm = arm
+        self.cooldown_s = cooldown_s
+        self.release_after_s = release_after_s
+        self.last_status: Dict[int, str] = {}
+        self.last_trigger_t: Dict[int, float] = {}
+        self._lock = threading.Lock()
+
+    def handle_line(self, line: str):
+        line = line.rstrip()
+        if not line:
+            return
+        # Always echo Saqr output so the user still sees the live stream.
+        print(line, flush=True)
+
+        m = EVENT_RE.match(line)
+        if not m:
+            return
+
+        track_id = int(m.group("id"))
+        status = m.group("status")
+
+        with self._lock:
+            prev = self.last_status.get(track_id)
+            self.last_status[track_id] = status
+
+            if status != DANGER_STATUS:
+                return
+
+            # Trigger only on transitions into UNSAFE, with per-id cooldown.
+            now = time.time()
+            last_t = self.last_trigger_t.get(track_id, 0.0)
+            transitioned = (prev != DANGER_STATUS)
+            cooled_down = (now - last_t) >= self.cooldown_s
+
+            if not (transitioned and cooled_down):
+                return
+
+            self.last_trigger_t[track_id] = now
+
+        # Run the arm action outside the lock so we don't block parsing.
+        try:
+            self.arm.reject(release_after=self.release_after_s)
+        except Exception as e:
+            print(f"[BRIDGE][ERR] arm reject failed: {e}", flush=True)
+
+
+# ── Saqr subprocess management ───────────────────────────────────────────────
+def build_saqr_cmd(saqr_extra_args: list[str]) -> list[str]:
+    if not SAQR_SCRIPT.exists():
+        sys.exit(f"[BRIDGE][FATAL] saqr.py not found at: {SAQR_SCRIPT}")
+    # -u for unbuffered stdout (so events arrive line-by-line).
+    return [sys.executable, "-u", str(SAQR_SCRIPT), *saqr_extra_args]
+
+
+def split_argv(argv: list[str]) -> tuple[list[str], list[str]]:
+    """Split bridge args from saqr passthrough args at the first '--'."""
+    if "--" in argv:
+        idx = argv.index("--")
+        return argv[:idx], argv[idx + 1 :]
+    return argv, []
+
+
+def main():
+    bridge_argv, saqr_extra = split_argv(sys.argv[1:])
+
+    ap = argparse.ArgumentParser(
+        description="Bridge Saqr PPE events to the G1 arm 'reject' action."
+    )
+    ap.add_argument("--iface", default=None,
+                    help="DDS network interface (e.g. enp3s0). Optional.")
+    ap.add_argument("--timeout", type=float, default=10.0,
+                    help="G1 arm client timeout (seconds).")
+    ap.add_argument("--cooldown", type=float, default=8.0,
+                    help="Per-track-id seconds before reject can re-trigger.")
+    ap.add_argument("--release-after", type=float, default=2.0,
+                    help="Seconds before auto-running 'release arm' (0 = never).")
+    ap.add_argument("--dry-run", action="store_true",
+                    help="Parse and decide but never call the SDK.")
+
+    # Convenience pass-throughs to saqr.py (you can also use `-- ...`).
+    ap.add_argument("--source", default=None,
+                    help="Saqr --source (0/realsense/path). Default: leave to saqr.")
+    ap.add_argument("--headless", action="store_true",
+                    help="Pass --headless to saqr.")
+    ap.add_argument("--saqr-conf", type=float, default=None,
+                    help="Pass --conf to saqr.")
+    ap.add_argument("--imgsz", type=int, default=None,
+                    help="Pass --imgsz to saqr.")
+    ap.add_argument("--device", default=None,
+                    help="Pass --device to saqr (e.g. cpu / 0 / cuda:0).")
+
+    args = ap.parse_args(bridge_argv)
+
+    # Build saqr args from convenience flags + raw passthrough.
+    saqr_args: list[str] = []
+    if args.source is not None:
+        saqr_args += ["--source", args.source]
+    if args.headless:
+        saqr_args += ["--headless"]
+    if args.saqr_conf is not None:
+        saqr_args += ["--conf", str(args.saqr_conf)]
+    if args.imgsz is not None:
+        saqr_args += ["--imgsz", str(args.imgsz)]
+    if args.device is not None:
+        saqr_args += ["--device", args.device]
+    saqr_args += saqr_extra
+
+    arm = ArmController(iface=args.iface, timeout=args.timeout, dry_run=args.dry_run)
+    bridge = Bridge(
+        arm=arm,
+        cooldown_s=args.cooldown,
+        release_after_s=args.release_after,
+    )
+
+    cmd = build_saqr_cmd(saqr_args)
+    print(f"[BRIDGE] launching: {' '.join(cmd)}", flush=True)
+    print(f"[BRIDGE] cwd: {SAQR_DIR}", flush=True)
+
+    env = os.environ.copy()
+    env["PYTHONUNBUFFERED"] = "1"
+
+    proc = subprocess.Popen(
+        cmd,
+        cwd=str(SAQR_DIR),
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        bufsize=1,
+        text=True,
+        env=env,
+    )
+
+    def _forward_signal(signum, _frame):
+        print(f"[BRIDGE] signal {signum} -> stopping saqr", flush=True)
+        try:
+            proc.send_signal(signum)
+        except Exception:
+            pass
+
+    signal.signal(signal.SIGINT, _forward_signal)
+    signal.signal(signal.SIGTERM, _forward_signal)
+
+    try:
+        assert proc.stdout is not None
+        for line in proc.stdout:
+            bridge.handle_line(line)
+    finally:
+        rc = proc.wait()
+        print(f"[BRIDGE] saqr exited rc={rc}", flush=True)
+        sys.exit(rc)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Controller/wireless_controller.py b/Controller/wireless_controller.py
new file mode 100644
index 0000000..995487f
--- /dev/null
+++ b/Controller/wireless_controller.py
@@ -0,0 +1,131 @@
+import time
+import sys
+import struct
+
+from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize
+
+# Uncomment the following two lines when using Go2、Go2-W、B2、B2-W、H1 robot
+# from unitree_sdk2py.idl.default import unitree_go_msg_dds__LowState_
+# from unitree_sdk2py.idl.unitree_go.msg.dds_ import LowState_
+
+# Uncomment the following two lines when using G1、H1-2 robot
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowState_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_
+
+class unitreeRemoteController:
+    def __init__(self):
+        # key
+        self.Lx = 0           
+        self.Rx = 0            
+        self.Ry = 0            
+        self.Ly = 0
+
+        # button
+        self.L1 = 0
+        self.L2 = 0
+        self.R1 = 0
+        self.R2 = 0
+        self.A = 0
+        self.B = 0
+        self.X = 0
+        self.Y = 0
+        self.Up = 0
+        self.Down = 0
+        self.Left = 0
+        self.Right = 0
+        self.Select = 0
+        self.F1 = 0
+        self.F3 = 0
+        self.Start = 0
+       
+    def parse_botton(self,data1,data2):
+        self.R1 = (data1 >> 0) & 1
+        self.L1 = (data1 >> 1) & 1
+        self.Start = (data1 >> 2) & 1
+        self.Select = (data1 >> 3) & 1
+        self.R2 = (data1 >> 4) & 1
+        self.L2 = (data1 >> 5) & 1
+        self.F1 = (data1 >> 6) & 1
+        self.F3 = (data1 >> 7) & 1
+        self.A = (data2 >> 0) & 1
+        self.B = (data2 >> 1) & 1
+        self.X = (data2 >> 2) & 1
+        self.Y = (data2 >> 3) & 1
+        self.Up = (data2 >> 4) & 1
+        self.Right = (data2 >> 5) & 1
+        self.Down = (data2 >> 6) & 1
+        self.Left = (data2 >> 7) & 1
+
+    def parse_key(self,data):
+        lx_offset = 4
+        self.Lx = struct.unpack('<f', data[lx_offset:lx_offset + 4])[0]
+        rx_offset = 8
+        self.Rx = struct.unpack('<f', data[rx_offset:rx_offset + 4])[0]
+        ry_offset = 12
+        self.Ry = struct.unpack('<f', data[ry_offset:ry_offset + 4])[0]
+        L2_offset = 16
+        L2 = struct.unpack('<f', data[L2_offset:L2_offset + 4])[0] # Placeholder，unused
+        ly_offset = 20
+        self.Ly = struct.unpack('<f', data[ly_offset:ly_offset + 4])[0]
+
+
+    def parse(self,remoteData):
+        self.parse_key(remoteData)
+        self.parse_botton(remoteData[2],remoteData[3])
+
+        print("debug unitreeRemoteController: ")
+        print("Lx:", self.Lx)
+        print("Rx:", self.Rx)
+        print("Ry:", self.Ry)
+        print("Ly:", self.Ly)
+
+        print("L1:", self.L1)
+        print("L2:", self.L2)
+        print("R1:", self.R1)
+        print("R2:", self.R2)
+        print("A:", self.A)
+        print("B:", self.B)
+        print("X:", self.X)
+        print("Y:", self.Y)
+        print("Up:", self.Up)
+        print("Down:", self.Down)
+        print("Left:", self.Left)
+        print("Right:", self.Right)
+        print("Select:", self.Select)
+        print("F1:", self.F1)
+        print("F3:", self.F3)
+        print("Start:", self.Start)
+        print("\n")
+
+        
+class Custom:
+    def __init__(self):
+        self.low_state = None 
+        self.remoteController = unitreeRemoteController()
+
+    def Init(self):
+        self.lowstate_subscriber = ChannelSubscriber("rt/lf/lowstate", LowState_)
+        self.lowstate_subscriber.Init(self.LowStateMessageHandler, 10)
+
+    
+    def LowStateMessageHandler(self, msg: LowState_):
+        self.low_state = msg
+        wireless_remote_data = self.low_state.wireless_remote
+        self.remoteController.parse(wireless_remote_data)
+
+
+if __name__ == '__main__':
+
+    print("WARNING: Please ensure there are no obstacles around the robot while running this example.")
+    input("Press Enter to continue...")
+
+    if len(sys.argv)>1:
+        ChannelFactoryInitialize(0, sys.argv[1])
+    else:
+        ChannelFactoryInitialize(0)
+
+    custom = Custom()
+    custom.Init()
+
+    while True:   
+        time.sleep(1)
\ No newline at end of file
diff --git a/Controller/wireless_controller_keys.py b/Controller/wireless_controller_keys.py
new file mode 100644
index 0000000..af3dc4d
--- /dev/null
+++ b/Controller/wireless_controller_keys.py
@@ -0,0 +1,276 @@
+import time
+import sys
+import struct
+from Logger import Logs
+
+from unitree_sdk2py.core.channel import ChannelSubscriber, ChannelFactoryInitialize
+from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowState_
+from unitree_sdk2py.idl.unitree_hg.msg.dds_ import LowState_
+
+# Initialize Logger
+controller_logs = Logs()
+controller_logs.LogEngine("G1_Logs", "Wireless_Controller_keys.log")
+
+
+
+"""_summary_
+
+1. Posture Switch (Modifier: Hold L2)
+
+These commands change the fundamental physical state or stance of the robot.
+
+    L2 + R2: Debug Mode
+
+    L2 + Y: Zero Torque / Zero Moment Mode
+
+    L2 + B: ① Damping Mode
+
+    L2 + UP: ② Lock Stand / Locked Standing
+
+    L2 + LEFT: ④ Seated Mode
+
+    L2 + X: ⑤ Lying and Standing (Lie -> Stand)
+
+    L2 + A: ⑥ Squat Switch (Squat <-> Stand)
+
+2. Interactional Functions (Modifier: Hold SELECT or Double-Click)
+
+These are social or "demo" movements.
+
+    SELECT + A: Handshake
+
+    SELECT + Y: Wave Hand
+
+    SELECT + X: Reject / Turn Around and Waves Hand
+
+    SELECT + B: Face Wave
+
+    Double-Click Y: Right Kiss
+
+    Double-Click X: Left Kiss
+
+    Double-Click A: Clap
+
+    Double-Click B: Right Raise
+
+    Double-Click UP: Flex Left
+
+    Double-Click DOWN: Flex Right
+
+    Double-Click LEFT: Flex Both
+
+3. Motion & Run Control (Modifier: Hold R2 or START)
+
+Commands related to locomotion and body leaning.
+
+    R2 + DOWN: Slow Running
+
+    R2 + UP: Fast Running
+
+    R2 + A: ③ Running Mode
+
+    R2 + B: ⑧ Climb Mode
+
+    START + UP: Forward Lean
+
+    START + DOWN: Backward Lean
+
+4. Main Operation & Special Modes (Modifier: Hold R1)
+
+Controls for complex body structures and specialized routines.
+
+    R1 + X: ⑦ Main Operation Control (1-DOF Waist)
+
+    R1 + Y: ⑧ Main Operation Control (3-DOF Waist)
+
+    R1 + SELECT: Kung Fu
+
+    R1 + A: Dance 1
+
+    R1 + B: Dance 2
+
+    R1 + X (alt): Dance 3
+
+    R1 + Y (alt): Roll Up
+
+5. System & Offset Settings
+
+Technical adjustments for calibration and UI feedback.
+
+    Click START: Stand / Standing
+
+    Double-Click START: Keep Stepping (Not Recommended)
+
+    Double-Click L1: High Speed Mode
+
+    Double-Click L2: Low Speed Mode
+
+    F1 (Pressed 3 times): Sound/Vibration Switch
+
+    F3 (Pressed 3 times): Sound/Vibration Toggle
+
+    Hold R1 + Click →: Left Offset
+
+    Hold R1 + Click ←: Right Offset
+
+    Hold R1 + Click ↓: Forward Offset
+
+    Hold R1 + Click ↑: Backward Offset
+
+6. Joysticks (Rockers)
+
+    Left Rocker (Lx, Ly): Translational movement (Forward/Backward/Sideways).
+
+    Right Rocker (Rx, Ry): Rotational movement (Yaw) or limb control in Operation Mode.
+    
+    
+    
+"""
+
+
+
+class unitreeRemoteController:
+    def __init__(self):
+        # Joysticks
+        self.Lx = 0; self.Rx = 0; self.Ry = 0; self.Ly = 0
+        # Buttons
+        self.L1 = 0; self.L2 = 0; self.R1 = 0; self.R2 = 0
+        self.A = 0; self.B = 0; self.X = 0; self.Y = 0
+        self.Up = 0; self.Down = 0; self.Left = 0; self.Right = 0
+        self.Select = 0; self.F1 = 0; self.F3 = 0; self.Start = 0
+       
+    def parse_botton(self, data1, data2):
+        self.R1 = (data1 >> 0) & 1; self.L1 = (data1 >> 1) & 1
+        self.Start = (data1 >> 2) & 1; self.Select = (data1 >> 3) & 1
+        self.R2 = (data1 >> 4) & 1; self.L2 = (data1 >> 5) & 1
+        self.F1 = (data1 >> 6) & 1; self.F3 = (data1 >> 7) & 1
+        self.A = (data2 >> 0) & 1; self.B = (data2 >> 1) & 1
+        self.X = (data2 >> 2) & 1; self.Y = (data2 >> 3) & 1
+        self.Up = (data2 >> 4) & 1; self.Right = (data2 >> 5) & 1
+        self.Down = (data2 >> 6) & 1; self.Left = (data2 >> 7) & 1
+
+    def parse_key(self, data):
+        offsets = [4, 8, 12, 20] # Lx, Rx, Ry, Ly
+        self.Lx, self.Rx, self.Ry, self.Ly = [struct.unpack('<f', data[o:o+4])[0] for o in offsets]
+
+    def parse(self, remoteData):
+        self.parse_key(remoteData)
+        self.parse_botton(remoteData[2], remoteData[3])
+
+    def get_state(self):
+        return self.__dict__.copy()
+
+class Custom:
+    def __init__(self):
+        self.remoteController = unitreeRemoteController()
+        self.last_state = None
+        self.click_times = {} 
+        self.CLICK_THRESHOLD = 0.35 
+
+    def Init(self):
+        self.print_usage_keys()
+        self.lowstate_subscriber = ChannelSubscriber("rt/lf/lowstate", LowState_)
+        self.lowstate_subscriber.Init(self.LowStateMessageHandler, 10)
+
+    def print_usage_keys(self):
+        usage = """
+------------------------------------------------------------------
+G1 REMOTE USAGE KEYS:
+[L2]      : Modifier for Posture (Squat, Sit, Stand, Damping)
+[R1]      : Modifier for Operation (Waist, Dance, Offset Comp)
+[R2]      : Modifier for Run Control (Fast, Slow, Climb, Running)
+[SELECT]  : Modifier for Interaction (Wave, Handshake, Reject)
+[START]   : Standing / Lean Control (Hold for Lean)
+------------------------------------------------------------------
+"""
+        controller_logs.print_and_log(usage, "info")
+
+    def LowStateMessageHandler(self, msg: LowState_):
+        self.remoteController.parse(msg.wireless_remote)
+        curr = self.remoteController.get_state()
+
+        # Detect Rising Edge (Initial Button Press)
+        if self.last_state:
+            for key, val in curr.items():
+                if key in ['Lx', 'Ly', 'Rx', 'Ry']: continue # Ignore joysticks for binary press log
+                if val == 1 and self.last_state[key] == 0:
+                    controller_logs.print_and_log(f"[PRESS] {key} button", "info")
+                    self.handle_multi_click(key, curr)
+
+        # Detect State Changes for Combinations/Joysticks
+        if curr != self.last_state:
+            self.process_actions(curr)
+            self.last_state = curr
+
+    def handle_multi_click(self, btn, state):
+        now = time.time()
+        prev_clicks, last_time = self.click_times.get(btn, (0, 0))
+        count = prev_clicks + 1 if now - last_time < self.CLICK_THRESHOLD else 1
+        self.click_times[btn] = (count, now)
+
+        # Double Click Actions
+        if count == 2:
+            if btn == 'L1': controller_logs.print_and_log("[ACTION] High Speed Mode (Double-Click L1)", "info")
+            if btn == 'L2': controller_logs.print_and_log("[ACTION] Low Speed Mode (Double-Click L2)", "info")
+            if btn == 'Start': controller_logs.print_and_log("[ACTION] Keep Stepping (Double-Click Start - Not Recommended)", "warning")
+            # Interactional Multi-clicks
+            if not any([state['Select'], state['L2'], state['R1']]):
+                mapping = {'Y':'Right Kiss', 'X':'Left Kiss', 'A':'Clap', 'B':'Right Raise', 
+                           'Up':'Flex Left', 'Down':'Flex Right', 'Left':'Flex Both'}
+                if btn in mapping: controller_logs.print_and_log(f"[ACTION] {mapping[btn]} (Double-Click {btn})", "info")
+        
+        # Triple Click System Settings
+        if count == 3:
+            if btn == 'F1': controller_logs.print_and_log("[ACTION] Sound/Vibration Switch (3x F1)", "info")
+            if btn == 'F3': controller_logs.print_and_log("[ACTION] Sound/Vibration Toggle (3x F3)", "info")
+
+    def process_actions(self, s):
+        # 1. Posture Switches (L2 Modifier)
+        if s['L2']:
+            mapping = {'R2':'Debug Mode', 'Y':'Zero Torque', 'B':'Damping Mode', 
+                       'Up':'Lock Stand', 'Left':'Seated Mode', 'X':'Lying/Standing', 'A':'Squat Switch'}
+            for btn, act in mapping.items():
+                if s[btn]: controller_logs.print_and_log(f"[ACTION] {act} (L2 + {btn})", "info"); return
+
+        # 2. Interactive (Select Modifier)
+        if s['Select']:
+            mapping = {'A':'Handshake', 'Y':'Wave Hand', 'X':'Reject / Turn Around', 'B':'Face Wave'}
+            for btn, act in mapping.items():
+                if s[btn]: controller_logs.print_and_log(f"[ACTION] {act} (Select + {btn})", "info"); return
+
+        # 3. Run Control (R2 Modifier)
+        if s['R2']:
+            mapping = {'Down':'Slow Running', 'Up':'Fast Running', 'A':'Running Mode', 'B':'Climb Mode'}
+            for btn, act in mapping.items():
+                if s[btn]: controller_logs.print_and_log(f"[ACTION] {act} (R2 + {btn})", "info"); return
+
+        # 4. Operation / Dance / Offset (R1 Modifier)
+        if s['R1']:
+            if s['Select']: controller_logs.print_and_log("[ACTION] Kung Fu (R1 + Select)", "info"); return
+            mapping = {'X':'Main Op (1-DOF) / Dance 3', 'Y':'Main Op (3-DOF) / Roll Up', 'A':'Dance 1', 'B':'Dance 2',
+                       'Right':'Left Offset', 'Left':'Right Offset', 'Down':'Forward Offset', 'Up':'Backward Offset'}
+            for btn, act in mapping.items():
+                if s[btn]: controller_logs.print_and_log(f"[ACTION] {act} (R1 + {btn})", "info"); return
+
+        # 5. Lean Control (Start Modifier)
+        if s['Start']:
+            if s['Up']: controller_logs.print_and_log("[ACTION] Forward Lean (Start + Up)", "info"); return
+            if s['Down']: controller_logs.print_and_log("[ACTION] Backward Lean (Start + Down)", "info"); return
+            # Default single click stand
+            if not self.last_state['Start']:
+                controller_logs.print_and_log("[ACTION] Standing (Click Start)", "info")
+
+        # 6. Joystick check (Deadzone 0.1)
+        for stick in ['Lx', 'Ly', 'Rx', 'Ry']:
+            if abs(s[stick]) > 0.1:
+                controller_logs.print_and_log(f"[STICK] {stick}: {s[stick]:.2f}", "debug")
+                break
+
+if __name__ == '__main__':
+    if len(sys.argv) > 1: ChannelFactoryInitialize(0, sys.argv[1])
+    else: ChannelFactoryInitialize(0)
+
+    custom = Custom()
+    custom.Init()
+    controller_logs.print_and_log("G1 Logger Ready. Monitoring inputs...", "info")
+    while True: time.sleep(0.01)
\ No newline at end of file
diff --git a/Doc/scripts/validate_map.py b/Doc/scripts/validate_map.py
new file mode 100644
index 0000000..7f10b27
--- /dev/null
+++ b/Doc/scripts/validate_map.py
@@ -0,0 +1,251 @@
+"""
+validate_map.py — Phase 1 acceptance harness for a SLAM-saved .ply map.
+
+Run:
+    python3 -m Doc.scripts.validate_map /path/to/map.ply
+        [--min-points 10000] [--max-clusters 1] [--min-z-span 1.0]
+
+On pass, writes `<map>.validated.json` next to the .ply. The GUI's
+production-gate (in SLAM_GUI) checks for this sidecar before allowing
+LIVE_NAV / EXTEND_MAP / LOCALIZE_MAP workflows on the map.
+
+Checks performed:
+  1. Point count >= min_points
+  2. DBSCAN cluster count <= max_clusters (default 1 — fragmented maps
+     indicate SLAM tracking loss; should be remediated with MapRefiner
+     before validation)
+  3. Z-axis span (ceiling height) >= min_z_span
+  4. X/Y bounding-box has at least min_xy_span on the smaller axis
+     (rejects degenerate "point-on-a-line" scans)
+
+Design notes:
+  - Like MapRefiner, the DBSCAN call runs in a subprocess so an Open3D
+    segfault doesn't crash the validator.
+  - The validation sidecar is intentionally small JSON so it can be
+    inspected, edited, or version-controlled if you need to override
+    a check.
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import pickle
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import numpy as np
+
+
+# Subprocess worker — mirrors MapRefiner's crash-safe pattern.
+def _worker_dbscan():
+    import open3d as o3d  # only imported inside subprocess
+    in_path = sys.argv[3]
+    out_path = sys.argv[4]
+    with open(in_path, "rb") as f:
+        payload = pickle.load(f)
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector(payload["points"])
+    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Error):
+        labels = np.array(pcd.cluster_dbscan(
+            eps=payload["eps"],
+            min_points=payload["min_points"],
+            print_progress=False,
+        ))
+    with open(out_path, "wb") as f:
+        pickle.dump({"labels": labels}, f)
+
+
+if len(sys.argv) > 2 and sys.argv[1] == "_worker" and sys.argv[2] == "dbscan":
+    _worker_dbscan()
+    sys.exit(0)
+
+
+def _run_dbscan_subprocess(points: np.ndarray, eps: float, min_pts: int,
+                           timeout_s: int = 90) -> np.ndarray | None:
+    """Run DBSCAN in an isolated subprocess. Returns labels or None on
+    timeout/error."""
+    with tempfile.TemporaryDirectory() as tmp:
+        in_p = os.path.join(tmp, "in.pkl")
+        out_p = os.path.join(tmp, "out.pkl")
+        with open(in_p, "wb") as fh:
+            pickle.dump(
+                {"points": points, "eps": eps, "min_points": min_pts},
+                fh,
+            )
+        try:
+            r = subprocess.run(
+                [sys.executable, __file__, "_worker", "dbscan", in_p, out_p],
+                timeout=timeout_s,
+                capture_output=True,
+            )
+        except subprocess.TimeoutExpired:
+            print(f"[validate_map] DBSCAN timed out after {timeout_s}s",
+                  file=sys.stderr)
+            return None
+        if r.returncode != 0 or not os.path.exists(out_p):
+            err = r.stderr.decode(errors="replace")[-300:] if r.stderr else "(no stderr)"
+            print(f"[validate_map] DBSCAN subprocess exit {r.returncode}: {err}",
+                  file=sys.stderr)
+            return None
+        with open(out_p, "rb") as fh:
+            return pickle.load(fh)["labels"]
+
+
+def _read_ply_xyz(path: str) -> np.ndarray:
+    """Read XYZ from PLY (ASCII or binary little-endian). No Open3D dep
+    in the parent process — keeps the validator robust against Open3D
+    segfaults during read."""
+    with open(path, "rb") as f:
+        header_lines = []
+        while True:
+            line = f.readline()
+            if not line:
+                raise ValueError("Unexpected EOF in PLY header")
+            header_lines.append(line)
+            if line.strip() == b"end_header":
+                break
+        header = b"".join(header_lines)
+        text = header.decode("ascii", errors="replace")
+        is_binary = "format binary_little_endian" in text
+        n_vertex = 0
+        props = []
+        in_vertex = False
+        for line in text.splitlines():
+            if line.startswith("element vertex"):
+                n_vertex = int(line.split()[-1])
+                in_vertex = True
+                continue
+            if line.startswith("element ") and in_vertex:
+                break
+            if in_vertex and line.startswith("property"):
+                parts = line.split()
+                props.append((parts[1], parts[2]))
+        if n_vertex == 0:
+            raise ValueError("No vertex element in PLY")
+
+        if is_binary:
+            type_map = {
+                "float": ("f4", 4), "float32": ("f4", 4), "double": ("f8", 8),
+                "uchar": ("u1", 1), "char": ("i1", 1),
+                "ushort": ("u2", 2), "short": ("i2", 2),
+                "uint": ("u4", 4), "int": ("i4", 4),
+            }
+            dtype_list = []
+            for t, name in props:
+                if t not in type_map:
+                    raise ValueError(f"Unknown PLY type: {t}")
+                dtype_list.append((name, "<" + type_map[t][0]))
+            arr = np.frombuffer(f.read(), dtype=np.dtype(dtype_list), count=n_vertex)
+            return np.column_stack([arr["x"], arr["y"], arr["z"]]).astype(np.float64)
+        # ASCII fallback
+        x_idx = next(i for i, (_, n) in enumerate(props) if n == "x")
+        y_idx = next(i for i, (_, n) in enumerate(props) if n == "y")
+        z_idx = next(i for i, (_, n) in enumerate(props) if n == "z")
+        xyz = np.zeros((n_vertex, 3), dtype=np.float64)
+        for i in range(n_vertex):
+            row = f.readline().decode("ascii").split()
+            xyz[i] = [float(row[x_idx]), float(row[y_idx]), float(row[z_idx])]
+        return xyz
+
+
+def _md5(path: str) -> str:
+    h = hashlib.md5()
+    with open(path, "rb") as fh:
+        for chunk in iter(lambda: fh.read(1 << 20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def validate(
+    ply_path: str,
+    *,
+    min_points: int = 10_000,
+    max_clusters: int = 1,
+    min_z_span: float = 1.0,
+    min_xy_span: float = 1.5,
+    dbscan_eps: float = 0.30,
+    dbscan_min_pts: int = 50,
+) -> dict:
+    """Run all checks. Returns a dict with `passed: bool` plus per-check
+    diagnostics. Caller decides whether to write the sidecar."""
+    result: dict = {
+        "path": str(ply_path),
+        "validated_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
+        "checks": {},
+        "passed": False,
+    }
+
+    pts = _read_ply_xyz(ply_path)
+    n = len(pts)
+    result["checks"]["point_count"] = {"value": int(n), "min": min_points, "ok": n >= min_points}
+
+    if n < min_points:
+        result["passed"] = False
+        return result
+
+    x_span = float(pts[:, 0].max() - pts[:, 0].min())
+    y_span = float(pts[:, 1].max() - pts[:, 1].min())
+    z_span = float(pts[:, 2].max() - pts[:, 2].min())
+    smaller_xy = min(x_span, y_span)
+    result["checks"]["z_span_m"] = {"value": z_span, "min": min_z_span, "ok": z_span >= min_z_span}
+    result["checks"]["xy_span_m"] = {"value": smaller_xy, "min": min_xy_span, "ok": smaller_xy >= min_xy_span}
+
+    labels = _run_dbscan_subprocess(pts, dbscan_eps, dbscan_min_pts)
+    if labels is None:
+        result["checks"]["clusters"] = {"value": None, "max": max_clusters, "ok": False, "error": "dbscan failed"}
+        result["passed"] = False
+        return result
+    n_clusters = int(len({int(l) for l in labels if l >= 0}))
+    result["checks"]["clusters"] = {"value": n_clusters, "max": max_clusters, "ok": n_clusters <= max_clusters}
+
+    result["md5"] = _md5(ply_path)
+    result["passed"] = all(c.get("ok", False) for c in result["checks"].values())
+    return result
+
+
+def write_sidecar(ply_path: str, validation: dict) -> str:
+    """Write `<ply_path>.validated.json`. The path format matches what
+    SLAM_GUI's `_map_is_validated` checks for."""
+    out = Path(ply_path).with_suffix(Path(ply_path).suffix + ".validated.json")
+    out.write_text(json.dumps(validation, indent=2), encoding="utf-8")
+    return str(out)
+
+
+def main() -> int:
+    p = argparse.ArgumentParser(description="Validate a SLAM-saved .ply map.")
+    p.add_argument("ply", help="Path to the .ply map file.")
+    p.add_argument("--min-points", type=int, default=10_000)
+    p.add_argument("--max-clusters", type=int, default=1)
+    p.add_argument("--min-z-span", type=float, default=1.0)
+    p.add_argument("--min-xy-span", type=float, default=1.5)
+    p.add_argument("--force", action="store_true", help="Write sidecar even if checks fail.")
+    args = p.parse_args()
+
+    if not os.path.exists(args.ply):
+        print(f"File not found: {args.ply}", file=sys.stderr)
+        return 2
+
+    res = validate(
+        args.ply,
+        min_points=args.min_points,
+        max_clusters=args.max_clusters,
+        min_z_span=args.min_z_span,
+        min_xy_span=args.min_xy_span,
+    )
+
+    print(json.dumps(res, indent=2))
+    if res["passed"] or args.force:
+        out = write_sidecar(args.ply, res)
+        print(f"sidecar written: {out}")
+        return 0
+    print("validation FAILED — sidecar NOT written (use --force to override)", file=sys.stderr)
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/Lidar/.bak-before-prod-sync/SLAM_Filter.py b/Lidar/.bak-before-prod-sync/SLAM_Filter.py
new file mode 100644
index 0000000..bc13f1c
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_Filter.py
@@ -0,0 +1,395 @@
+# SLAM_Filter.py
+from __future__ import annotations
+
+import json
+import os
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict
+import numpy as np
+
+
+def load_slam_config() -> dict:
+    import json, os
+    from pathlib import Path
+
+    cfg_path = os.environ.get("SLAM_CONFIG", "").strip()
+    p = Path(cfg_path) if cfg_path else (Path(__file__).resolve().parent / "SLAM_Config.json")
+
+    if not p.exists():
+        raise FileNotFoundError(f"Missing config file: {p}")
+
+    text = p.read_text(encoding="utf-8").strip()
+    if not text:
+        raise RuntimeError("SLAM_Config.json is empty.")
+
+    return json.loads(text)
+
+
+
+@dataclass
+class FilterConfig:
+    voxel_size: float
+    hit_threshold: int
+    decay_seconds: float
+    max_voxels: int
+
+    @staticmethod
+    def from_config_file() -> "FilterConfig":
+        cfg = load_slam_config()
+        return FilterConfig(
+            voxel_size=float(cfg["filter"]["voxel_size"]),
+            hit_threshold=int(cfg["filter"]["hits_threshold"]),
+            decay_seconds=float(cfg["filter"]["persistence"]["decay_seconds"]),
+            max_voxels=int(cfg["filter"]["persistence"]["max_voxels"]),
+        )
+
+
+class VoxelPersistenceFilter:
+    """
+    Voxel hit-count persistence filter.
+    No hardcoded params: everything comes from SLAM_Config.jsonl.
+    """
+
+    def __init__(self, cfg: FilterConfig | None = None):
+        self.cfg = cfg if cfg is not None else FilterConfig.from_config_file()
+        self._count: dict[tuple[int, int, int], int] = {}
+        self._last: dict[tuple[int, int, int], float] = {}
+        self._sum: dict[tuple[int, int, int], np.ndarray] = {}
+        self._n: dict[tuple[int, int, int], int] = {}
+
+    def reset(self):
+        self._count.clear()
+        self._last.clear()
+        self._sum.clear()
+        self._n.clear()
+
+    def _voxel_keys(self, pts: np.ndarray) -> np.ndarray:
+        vs = float(self.cfg.voxel_size)
+        return np.floor(pts / vs).astype(np.int32)
+
+    def update(self, points_world: np.ndarray, now: float | None = None) -> None:
+        if points_world is None or len(points_world) == 0:
+            return
+
+        now = time.time() if now is None else float(now)
+        keys = self._voxel_keys(points_world)
+        uniq, inv = np.unique(keys, axis=0, return_inverse=True)
+
+        # Vectorized per-voxel means; previous np.where-in-loop was O(N*U).
+        sums = np.zeros((len(uniq), 3), dtype=np.float64)
+        counts = np.zeros((len(uniq),), dtype=np.int64)
+        np.add.at(sums, inv, points_world.astype(np.float64, copy=False))
+        np.add.at(counts, inv, 1)
+        means = sums / np.maximum(counts[:, None], 1)
+
+        for i, k in enumerate(uniq):
+            k_t = (int(k[0]), int(k[1]), int(k[2]))
+            self._count[k_t] = self._count.get(k_t, 0) + 1
+            self._last[k_t] = now
+            if k_t not in self._sum:
+                self._sum[k_t] = means[i].astype(np.float64, copy=True)
+                self._n[k_t] = 1
+            else:
+                self._sum[k_t] += means[i]
+                self._n[k_t] += 1
+
+        self._decay(now)
+
+        if len(self._count) > int(self.cfg.max_voxels):
+            self._aggressive_prune()
+
+    def _decay(self, now: float) -> None:
+        ttl = float(self.cfg.decay_seconds)
+        to_del = []
+        for k, t_last in self._last.items():
+            if (now - t_last) > ttl:
+                to_del.append(k)
+        for k in to_del:
+            self._count.pop(k, None)
+            self._last.pop(k, None)
+            self._sum.pop(k, None)
+            self._n.pop(k, None)
+
+    def _aggressive_prune(self) -> None:
+        items = sorted(self._count.items(), key=lambda kv: kv[1], reverse=True)
+        keep_n = int(int(self.cfg.max_voxels) * 0.8)
+        keep = set(k for k, _ in items[:keep_n])
+
+        for k in list(self._count.keys()):
+            if k not in keep:
+                self._count.pop(k, None)
+                self._last.pop(k, None)
+                self._sum.pop(k, None)
+                self._n.pop(k, None)
+
+    def get_stable_points(self) -> np.ndarray:
+        thr = int(self.cfg.hit_threshold)
+        pts = []
+        for k, c in self._count.items():
+            if c >= thr:
+                s = self._sum.get(k)
+                n = self._n.get(k, 1)
+                if s is not None:
+                    pts.append(s / max(n, 1))
+        if not pts:
+            return np.zeros((0, 3), dtype=np.float32)
+        return np.asarray(pts, dtype=np.float32)
+
+    def seed_stable_points(
+        self,
+        points_world: np.ndarray,
+        now: float | None = None,
+        hit_count: int | None = None,
+        clear_existing: bool = True,
+    ) -> None:
+        """
+        Seed filter state from already-stable world-frame points.
+        Useful after global pose correction (loop-closure optimization).
+        """
+        if points_world is None or len(points_world) == 0:
+            if clear_existing:
+                self.reset()
+            return
+        if clear_existing:
+            self.reset()
+
+        now_v = time.time() if now is None else float(now)
+        hc = int(hit_count if hit_count is not None else self.cfg.hit_threshold)
+        hc = max(1, hc)
+
+        pts = np.asarray(points_world, dtype=np.float32)
+        keys = self._voxel_keys(pts)
+        uniq, inv = np.unique(keys, axis=0, return_inverse=True)
+
+        for i, k in enumerate(uniq):
+            k_t = (int(k[0]), int(k[1]), int(k[2]))
+            idx = np.where(inv == i)[0]
+            mean = pts[idx].mean(axis=0)
+
+            self._count[k_t] = max(self._count.get(k_t, 0), hc)
+            self._last[k_t] = now_v
+            self._sum[k_t] = mean.copy()
+            self._n[k_t] = 1
+
+        self._decay(now_v)
+        if len(self._count) > int(self.cfg.max_voxels):
+            self._aggressive_prune()
+
+    def stats(self) -> dict:
+        thr = int(self.cfg.hit_threshold)
+        stable = sum(1 for c in self._count.values() if c >= thr)
+        return {
+            "voxels_total": len(self._count),
+            "voxels_stable": stable,
+            "hit_threshold": thr,
+            "voxel_size": float(self.cfg.voxel_size),
+            "decay_seconds": float(self.cfg.decay_seconds),
+            "max_voxels": int(self.cfg.max_voxels),
+        }
+
+
+@dataclass
+class IndoorMapQualityConfig:
+    enabled: bool = True
+    near_min_range_m: float = 0.15
+    ray_consistency_enabled: bool = True
+    ray_bin_deg: float = 1.0
+    ray_elev_bin_deg: float = 4.0
+    ray_max_behind_m: float = 0.25
+    ray_keep_n: int = 1
+    world_z_clip_enabled: bool = False
+    world_z_min_m: float = -2.0
+    world_z_max_m: float = 3.0
+    outlier_filter_enabled: bool = False
+    outlier_voxel_m: float = 0.12
+    outlier_min_points: int = 2
+    # Robot body exclusion box (sensor frame) — removes self-returns from robot body.
+    # Tuned for Unitree G1 Edu with chest-mounted Livox MID-360.
+    body_exclusion_enabled: bool = False
+    body_x_min_m: float = -0.20
+    body_x_max_m: float = 0.35
+    body_y_min_m: float = -0.25
+    body_y_max_m: float = 0.25
+    body_z_min_m: float = -1.30
+    body_z_max_m: float = 0.15
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "IndoorMapQualityConfig":
+        src = d or {}
+        return IndoorMapQualityConfig(
+            enabled=bool(src.get("enabled", True)),
+            near_min_range_m=max(0.0, float(src.get("near_min_range_m", 0.15))),
+            ray_consistency_enabled=bool(src.get("ray_consistency_enabled", True)),
+            ray_bin_deg=max(0.2, float(src.get("ray_bin_deg", 1.0))),
+            ray_elev_bin_deg=max(0.0, float(src.get("ray_elev_bin_deg", 4.0))),
+            ray_max_behind_m=max(0.0, float(src.get("ray_max_behind_m", 0.25))),
+            ray_keep_n=max(1, int(src.get("ray_keep_n", 1))),
+            world_z_clip_enabled=bool(src.get("world_z_clip_enabled", False)),
+            world_z_min_m=float(src.get("world_z_min_m", -2.0)),
+            world_z_max_m=float(src.get("world_z_max_m", 3.0)),
+            outlier_filter_enabled=bool(src.get("outlier_filter_enabled", False)),
+            outlier_voxel_m=max(0.02, float(src.get("outlier_voxel_m", 0.12))),
+            outlier_min_points=max(1, int(src.get("outlier_min_points", 2))),
+            body_exclusion_enabled=bool(src.get("body_exclusion_enabled", False)),
+            body_x_min_m=float(src.get("body_x_min_m", -0.20)),
+            body_x_max_m=float(src.get("body_x_max_m", 0.35)),
+            body_y_min_m=float(src.get("body_y_min_m", -0.25)),
+            body_y_max_m=float(src.get("body_y_max_m", 0.25)),
+            body_z_min_m=float(src.get("body_z_min_m", -1.30)),
+            body_z_max_m=float(src.get("body_z_max_m", 0.15)),
+        )
+
+
+class IndoorMapQualityFilter:
+    """
+    Lightweight indoor map-quality filters intended for real-time use.
+    """
+
+    def __init__(self, cfg: IndoorMapQualityConfig):
+        self.cfg = cfg
+
+    @staticmethod
+    def _finite(points: np.ndarray) -> np.ndarray:
+        if points is None or len(points) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        pts = np.asarray(points, dtype=np.float32)
+        m = np.isfinite(pts).all(axis=1)
+        return pts[m]
+
+    def _remove_sparse_voxels(self, points: np.ndarray) -> np.ndarray:
+        if points is None or len(points) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        voxel = float(self.cfg.outlier_voxel_m)
+        keys = np.floor(points / voxel).astype(np.int32)
+        uniq, inv, counts = np.unique(keys, axis=0, return_inverse=True, return_counts=True)
+        del uniq  # only counts/inv are needed
+        keep = counts[inv] >= int(self.cfg.outlier_min_points)
+        return points[keep]
+
+    def _ray_consistency_filter(self, points: np.ndarray) -> np.ndarray:
+        """
+        Keep nearest returns per azimuth bin (+small behind margin) to suppress
+        echoes and duplicated points that appear behind wall lines.
+        Uses a vectorized fast path for keep_n=1 to keep CPU usage low.
+        """
+        if points is None or len(points) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        if not self.cfg.ray_consistency_enabled:
+            return points
+
+        pts = np.asarray(points, dtype=np.float32)
+        if len(pts) < 80:
+            return pts
+
+        xy = pts[:, :2]
+        r = np.linalg.norm(xy, axis=1)
+        valid = np.isfinite(r) & (r > 1e-4)
+        if not np.all(valid):
+            pts = pts[valid]
+            r = r[valid]
+        if len(pts) < 40:
+            return pts
+
+        bin_rad = np.deg2rad(float(self.cfg.ray_bin_deg))
+        az = np.arctan2(pts[:, 1], pts[:, 0])
+        bins_az = np.floor((az + np.pi) / max(1e-6, bin_rad)).astype(np.int32)
+        elev_bin_deg = float(self.cfg.ray_elev_bin_deg)
+        if elev_bin_deg > 0.0:
+            elev_rad = np.deg2rad(max(0.2, elev_bin_deg))
+            elev = np.arctan2(pts[:, 2], np.maximum(r, 1e-4))
+            bins_el = np.floor((elev + 0.5 * np.pi) / elev_rad).astype(np.int32)
+            n_az = int(np.max(bins_az)) + 1 if len(bins_az) > 0 else 1
+            bins = bins_az + (np.maximum(0, bins_el) * max(1, n_az))
+        else:
+            bins = bins_az
+        margin = float(self.cfg.ray_max_behind_m)
+        keep_n = int(self.cfg.ray_keep_n)
+        n_bins = int(np.max(bins)) + 1 if len(bins) > 0 else 0
+        if n_bins <= 0:
+            return pts
+
+        if keep_n <= 1:
+            # Fast vectorized mode: nearest hit per bin + margin.
+            min_r = np.full((n_bins,), np.inf, dtype=np.float32)
+            np.minimum.at(min_r, bins, r.astype(np.float32, copy=False))
+            keep = r <= (min_r[bins] + margin)
+        else:
+            # Fallback for multi-return mode (rare; higher CPU).
+            order = np.lexsort((r, bins))
+            bins_s = bins[order]
+            r_s = r[order]
+            keep_sorted = np.zeros((len(order),), dtype=bool)
+            i = 0
+            n = int(len(order))
+            while i < n:
+                j = i + 1
+                b = int(bins_s[i])
+                while j < n and int(bins_s[j]) == b:
+                    j += 1
+
+                rr = r_s[i:j]
+                if len(rr) <= keep_n:
+                    keep_sorted[i:j] = True
+                else:
+                    base = float(rr[min(len(rr) - 1, keep_n - 1)])
+                    cutoff = base + margin
+                    local = rr <= cutoff
+                    local[:keep_n] = True
+                    keep_sorted[i:j] = local
+                i = j
+            keep = np.zeros((len(pts),), dtype=bool)
+            keep[order] = keep_sorted
+
+        out = pts[keep]
+        # Safety fallback: if filter is too aggressive, keep original frame.
+        if len(out) < max(40, int(0.15 * len(pts))):
+            return pts
+        return out
+
+    def _exclude_body_box(self, points: np.ndarray) -> np.ndarray:
+        """Remove points inside the robot body bounding box (sensor frame).
+        Prevents G1 arm/torso self-returns from polluting the map.
+        """
+        inside = (
+            (points[:, 0] >= self.cfg.body_x_min_m) & (points[:, 0] <= self.cfg.body_x_max_m) &
+            (points[:, 1] >= self.cfg.body_y_min_m) & (points[:, 1] <= self.cfg.body_y_max_m) &
+            (points[:, 2] >= self.cfg.body_z_min_m) & (points[:, 2] <= self.cfg.body_z_max_m)
+        )
+        return points[~inside]
+
+    def apply_sensor(self, points_sensor: np.ndarray) -> np.ndarray:
+        pts = self._finite(points_sensor)
+        if not self.cfg.enabled or len(pts) == 0:
+            return pts
+
+        r2 = np.einsum("ij,ij->i", pts, pts)
+        min_r2 = float(self.cfg.near_min_range_m) ** 2
+        pts = pts[r2 >= min_r2]
+        if len(pts) == 0:
+            return pts
+
+        if self.cfg.body_exclusion_enabled:
+            pts = self._exclude_body_box(pts)
+            if len(pts) == 0:
+                return pts
+
+        pts = self._ray_consistency_filter(pts)
+        if len(pts) == 0:
+            return pts
+
+        if self.cfg.outlier_filter_enabled:
+            pts = self._remove_sparse_voxels(pts)
+        return pts
+
+    def apply_world(self, points_world: np.ndarray) -> np.ndarray:
+        pts = self._finite(points_world)
+        if not self.cfg.enabled or len(pts) == 0:
+            return pts
+        if self.cfg.world_z_clip_enabled:
+            zmin = float(self.cfg.world_z_min_m)
+            zmax = float(self.cfg.world_z_max_m)
+            if zmax > zmin:
+                pts = pts[(pts[:, 2] >= zmin) & (pts[:, 2] <= zmax)]
+        return pts
diff --git a/Lidar/.bak-before-prod-sync/SLAM_GUI.py b/Lidar/.bak-before-prod-sync/SLAM_GUI.py
new file mode 100644
index 0000000..516f01f
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_GUI.py
@@ -0,0 +1,2427 @@
+# SLAM_GUI.py
+from __future__ import annotations
+
+import json
+import os
+import sys
+import signal
+import time
+from pathlib import Path
+
+import numpy as np
+
+from PyQt6.QtWidgets import (
+    QApplication, QMainWindow, QVBoxLayout, QHBoxLayout,
+    QLabel, QLineEdit, QPushButton, QFileDialog, QMessageBox,
+    QFrame, QSplitter, QCheckBox, QGraphicsDropShadowEffect, QScrollArea
+)
+from PyQt6.QtCore import Qt, QTimer, QThread, pyqtSignal
+
+import pyqtgraph.opengl as gl
+
+from SLAM_engine import SlamEngineClient, load_slam_config
+
+
+def apply_card_shadow(widget: QFrame):
+    shadow = QGraphicsDropShadowEffect(widget)
+    shadow.setBlurRadius(22)
+    shadow.setOffset(0, 6)
+    shadow.setColor(Qt.GlobalColor.black)
+    widget.setGraphicsEffect(shadow)
+
+
+def pill_button(bg: str, fg: str = "white") -> str:
+    return f"""
+    QPushButton {{
+        background-color: {bg};
+        color: {fg};
+        font-weight: 800;
+        border: 1px solid rgba(255,255,255,0.08);
+        border-radius: 10px;
+        padding: 10px 12px;
+    }}
+    QPushButton:hover {{ border: 1px solid rgba(255,255,255,0.18); }}
+    QPushButton:disabled {{
+        background-color: rgba(255,255,255,0.08);
+        color: rgba(255,255,255,0.35);
+        border: 1px solid rgba(255,255,255,0.05);
+    }}
+    """
+
+
+def card_style() -> str:
+    return """
+    QFrame#Card {
+        background-color: rgba(255,255,255,0.04);
+        border: 1px solid rgba(255,255,255,0.06);
+        border-radius: 14px;
+    }
+    QLabel#CardTitle { color: rgba(255,255,255,0.75); font-weight: 800; }
+    QLineEdit {
+        background-color: rgba(0,0,0,0.30);
+        border: 1px solid rgba(255,255,255,0.10);
+        border-radius: 10px;
+        padding: 10px 10px;
+        color: white;
+        font-weight: 600;
+    }
+    QLineEdit:disabled {
+        color: rgba(255,255,255,0.40);
+        background-color: rgba(255,255,255,0.05);
+        border: 1px solid rgba(255,255,255,0.05);
+    }
+    QCheckBox { color: white; font-weight: 700; spacing: 10px; }
+    QCheckBox:disabled { color: rgba(255,255,255,0.40); }
+    """
+
+
+class SLAMGLViewWidget(gl.GLViewWidget):
+    mapClicked = pyqtSignal(float, float, float)
+    mapPickDrag = pyqtSignal(float, float, float, bool)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.pick_mode = False
+        self.pick_plane_z = 0.0
+        self._pick_dragging = False
+        self.pick_points = None
+        self.pick_screen_max_px = 140.0
+
+    @staticmethod
+    def _qmat_to_np_candidates(m) -> list[np.ndarray]:
+        try:
+            data = np.array(m.copyDataTo(), dtype=np.float64)
+            if data.size != 16:
+                return [np.eye(4, dtype=np.float64)]
+            mats = [
+                data.reshape((4, 4), order="F"),
+                data.reshape((4, 4), order="C"),
+            ]
+            out: list[np.ndarray] = []
+            for mm in mats:
+                if np.isfinite(mm).all():
+                    out.append(np.asarray(mm, dtype=np.float64))
+            return out if out else [np.eye(4, dtype=np.float64)]
+        except Exception:
+            return [np.eye(4, dtype=np.float64)]
+
+    def _screen_to_world_on_plane(self, sx: float, sy: float, plane_z: float = 0.0):
+        w = max(2, int(self.width()))
+        h = max(2, int(self.height()))
+        x_ndc = (2.0 * float(sx) / float(w)) - 1.0
+        y_ndc = 1.0 - (2.0 * float(sy) / float(h))
+
+        try:
+            cam = self.cameraPosition()
+            ctr = self.opts["center"]
+            cam_p = np.asarray([float(cam.x()), float(cam.y()), float(cam.z())], dtype=np.float64)
+            ctr_p = np.asarray([float(ctr.x()), float(ctr.y()), float(ctr.z())], dtype=np.float64)
+            fwd = ctr_p - cam_p
+            nf = float(np.linalg.norm(fwd))
+            if nf < 1e-9:
+                raise ValueError("degenerate camera forward")
+            fwd = fwd / nf
+            up_world = np.asarray([0.0, 0.0, 1.0], dtype=np.float64)
+            right = np.cross(fwd, up_world)
+            nr = float(np.linalg.norm(right))
+            if nr < 1e-6:
+                up_world = np.asarray([0.0, 1.0, 0.0], dtype=np.float64)
+                right = np.cross(fwd, up_world)
+                nr = float(np.linalg.norm(right))
+            if nr < 1e-9:
+                raise ValueError("degenerate camera right")
+            right = right / nr
+            up = np.cross(right, fwd)
+            nu = float(np.linalg.norm(up))
+            if nu < 1e-9:
+                raise ValueError("degenerate camera up")
+            up = up / nu
+            fov_rad = float(np.deg2rad(float(self.opts.get("fov", 60.0))))
+            sx_scale = float(np.tan(0.5 * fov_rad))
+            sy_scale = sx_scale * (float(h) / float(w))
+            ray = fwd + (x_ndc * sx_scale * right) + (y_ndc * sy_scale * up)
+            nray = float(np.linalg.norm(ray))
+            if nray < 1e-9:
+                raise ValueError("degenerate pick ray")
+            ray = ray / nray
+            if abs(float(ray[2])) < 1e-9:
+                return None
+            t = (float(plane_z) - float(cam_p[2])) / float(ray[2])
+            if t < 0.0:
+                return None
+            pt = cam_p + (t * ray)
+            if np.isfinite(pt).all():
+                return np.asarray(pt, dtype=np.float64)
+        except Exception:
+            pass
+
+        # Fallback: matrix unproject solver
+        viewport = self.getViewport()
+        region = viewport
+        proj_cands = self._qmat_to_np_candidates(self.projectionMatrix(region, viewport))
+        view_cands = self._qmat_to_np_candidates(self.viewMatrix())
+        near = np.array([x_ndc, y_ndc, -1.0, 1.0], dtype=np.float64)
+        far = np.array([x_ndc, y_ndc, 1.0, 1.0], dtype=np.float64)
+        best_pt = None
+        best_err = 1e18
+        for proj in proj_cands:
+            for view in view_cands:
+                for m in (proj @ view, view @ proj):
+                    try:
+                        inv = np.linalg.inv(m)
+                    except Exception:
+                        continue
+                    p0 = inv @ near
+                    p1 = inv @ far
+                    if abs(float(p0[3])) < 1e-9 or abs(float(p1[3])) < 1e-9:
+                        continue
+                    p0 = p0[:3] / p0[3]
+                    p1 = p1[:3] / p1[3]
+                    ray = p1 - p0
+                    if abs(float(ray[2])) < 1e-9:
+                        continue
+                    t = (float(plane_z) - float(p0[2])) / float(ray[2])
+                    if t < 0.0:
+                        continue
+                    pt = p0 + (t * ray)
+                    if not np.isfinite(pt).all():
+                        continue
+                    clip = m @ np.array([float(pt[0]), float(pt[1]), float(pt[2]), 1.0], dtype=np.float64)
+                    if abs(float(clip[3])) < 1e-9:
+                        continue
+                    ndc = clip[:3] / clip[3]
+                    sx2 = ((float(ndc[0]) + 1.0) * 0.5) * float(w)
+                    sy2 = ((1.0 - float(ndc[1])) * 0.5) * float(h)
+                    err = (sx2 - float(sx)) ** 2 + (sy2 - float(sy)) ** 2
+                    if err < best_err:
+                        best_err = float(err)
+                        best_pt = np.asarray(pt, dtype=np.float64)
+        return best_pt
+
+    def _screen_pick_from_points(self, sx: float, sy: float):
+        pts = self.pick_points
+        if pts is None:
+            return None
+        arr = np.asarray(pts, dtype=np.float64)
+        if arr.ndim != 2 or arr.shape[1] != 3 or len(arr) == 0:
+            return None
+        w = max(2, int(self.width()))
+        h = max(2, int(self.height()))
+        try:
+            cam = self.cameraPosition()
+            ctr = self.opts["center"]
+            cam_p = np.asarray([float(cam.x()), float(cam.y()), float(cam.z())], dtype=np.float64)
+            ctr_p = np.asarray([float(ctr.x()), float(ctr.y()), float(ctr.z())], dtype=np.float64)
+            fwd = ctr_p - cam_p
+            nf = float(np.linalg.norm(fwd))
+            if nf < 1e-9:
+                return None
+            fwd = fwd / nf
+            up_world = np.asarray([0.0, 0.0, 1.0], dtype=np.float64)
+            right = np.cross(fwd, up_world)
+            nr = float(np.linalg.norm(right))
+            if nr < 1e-6:
+                up_world = np.asarray([0.0, 1.0, 0.0], dtype=np.float64)
+                right = np.cross(fwd, up_world)
+                nr = float(np.linalg.norm(right))
+            if nr < 1e-9:
+                return None
+            right = right / nr
+            up = np.cross(right, fwd)
+            nu = float(np.linalg.norm(up))
+            if nu < 1e-9:
+                return None
+            up = up / nu
+            fov_rad = float(np.deg2rad(float(self.opts.get("fov", 60.0))))
+            sx_scale = float(np.tan(0.5 * fov_rad))
+            sy_scale = sx_scale * (float(h) / float(w))
+            rel = arr - cam_p.reshape((1, 3))
+            zc = rel @ fwd
+            keep = zc > 1e-6
+            if not np.any(keep):
+                return None
+            rel = rel[keep]
+            zc = zc[keep]
+            pts_k = arr[keep]
+            xc = rel @ right
+            yc = rel @ up
+            x_ndc = xc / (zc * sx_scale)
+            y_ndc = yc / (zc * sy_scale)
+            sxv = ((x_ndc + 1.0) * 0.5) * float(w)
+            syv = ((1.0 - y_ndc) * 0.5) * float(h)
+            d2 = (sxv - float(sx)) ** 2 + (syv - float(sy)) ** 2
+            idx = int(np.argmin(d2))
+            if float(d2[idx]) > float(self.pick_screen_max_px) * float(self.pick_screen_max_px):
+                return None
+            p = pts_k[idx]
+            if np.isfinite(p).all():
+                return p.astype(np.float64)
+            return None
+        except Exception:
+            return None
+
+    def mousePressEvent(self, ev):
+        lpos = ev.position() if hasattr(ev, "position") else ev.localPos()
+        self.mousePos = lpos
+        if self.pick_mode and ev.button() == Qt.MouseButton.LeftButton:
+            pt = self._screen_pick_from_points(float(lpos.x()), float(lpos.y()))
+            if pt is None:
+                pt = self._screen_to_world_on_plane(float(lpos.x()), float(lpos.y()), float(self.pick_plane_z))
+            if pt is not None:
+                self._pick_dragging = False
+                self.mapPickDrag.emit(float(pt[0]), float(pt[1]), float(pt[2]), True)
+                self.mapClicked.emit(float(pt[0]), float(pt[1]), float(pt[2]))
+                ev.accept()
+                return
+        super().mousePressEvent(ev)
+
+    def mouseMoveEvent(self, ev):
+        lpos = ev.position() if hasattr(ev, "position") else ev.localPos()
+        if not hasattr(self, "mousePos"):
+            self.mousePos = lpos
+        diff = lpos - self.mousePos
+        self.mousePos = lpos
+
+        buttons = ev.buttons()
+        mods = ev.modifiers()
+
+        if self.pick_mode and self._pick_dragging and (buttons & Qt.MouseButton.LeftButton):
+            pt = self._screen_to_world_on_plane(float(lpos.x()), float(lpos.y()), float(self.pick_plane_z))
+            if pt is not None:
+                self.mapPickDrag.emit(float(pt[0]), float(pt[1]), float(pt[2]), False)
+            ev.accept()
+            return
+
+        # Keep default left-drag behavior.
+        if buttons & Qt.MouseButton.LeftButton:
+            if mods & Qt.KeyboardModifier.ControlModifier:
+                self.pan(diff.x(), diff.y(), 0, relative="view")
+            else:
+                self.orbit(-diff.x(), diff.y())
+            return
+
+        # Add right-drag panning so touchpads/mice without middle-click can move map.
+        if buttons & Qt.MouseButton.RightButton:
+            if mods & Qt.KeyboardModifier.ControlModifier:
+                self.pan(diff.x(), 0, diff.y(), relative="view-upright")
+            else:
+                self.pan(diff.x(), diff.y(), 0, relative="view-upright")
+            return
+
+        # Keep default middle-drag panning behavior.
+        if buttons & Qt.MouseButton.MiddleButton:
+            if mods & Qt.KeyboardModifier.ControlModifier:
+                self.pan(diff.x(), 0, diff.y(), relative="view-upright")
+            else:
+                self.pan(diff.x(), diff.y(), 0, relative="view-upright")
+
+    def mouseReleaseEvent(self, ev):
+        lpos = ev.position() if hasattr(ev, "position") else ev.localPos()
+        if self.pick_mode and ev.button() == Qt.MouseButton.LeftButton and self._pick_dragging:
+            self._pick_dragging = False
+            pt = self._screen_to_world_on_plane(float(lpos.x()), float(lpos.y()), float(self.pick_plane_z))
+            if pt is not None:
+                self.mapPickDrag.emit(float(pt[0]), float(pt[1]), float(pt[2]), True)
+                self.mapClicked.emit(float(pt[0]), float(pt[1]), float(pt[2]))
+            ev.accept()
+            return
+        super().mouseReleaseEvent(ev)
+
+
+class RefLoaderThread(QThread):
+    loaded = pyqtSignal(object, object, str)  # pts, colors, filename
+    failed = pyqtSignal(str)
+
+    def __init__(self, filename: str, display_voxel: float, color_mode: str = "GRAY"):
+        super().__init__()
+        self.filename = filename
+        self.display_voxel = float(display_voxel)
+        self.color_mode = str(color_mode).upper().strip()
+
+    @staticmethod
+    def _height_colors_fast(pts: np.ndarray, alpha: float = 0.40) -> np.ndarray:
+        if pts is None or len(pts) == 0:
+            return np.zeros((0, 4), dtype=np.float32)
+        z = pts[:, 2]
+        zmin = float(np.min(z))
+        zmax = float(np.max(z))
+        span = max(1e-6, zmax - zmin)
+        norm = np.clip((z - zmin) / span, 0.0, 1.0).astype(np.float32)
+        colors = np.zeros((len(pts), 4), dtype=np.float32)
+        colors[:, 0] = norm
+        colors[:, 1] = 1.0 - np.abs(norm - 0.5) * 2.0
+        colors[:, 2] = 1.0 - norm
+        colors[:, 3] = alpha
+        return colors
+
+    def run(self):
+        try:
+            import open3d as o3d
+            pcd = o3d.io.read_point_cloud(self.filename)
+            if self.display_voxel > 0:
+                pcd = pcd.voxel_down_sample(self.display_voxel)
+            pts = np.asarray(pcd.points).astype(np.float32)
+            if len(pts) < 2:
+                raise RuntimeError("Ref map is empty.")
+
+            mode = self.color_mode if self.color_mode in ("GRAY", "HEIGHT", "ORIGINAL") else "GRAY"
+            colors = np.zeros((len(pts), 4), dtype=np.float32)
+            colors[:, 3] = 0.40
+            if mode == "HEIGHT":
+                colors = self._height_colors_fast(pts, alpha=0.40)
+            elif mode == "ORIGINAL":
+                src = np.asarray(pcd.colors)
+                if src is not None and len(src) == len(pts) and len(src) > 0:
+                    src_rgb = np.asarray(src, dtype=np.float32)
+                    colors[:, :3] = np.clip(src_rgb[:, :3], 0.0, 1.0)
+                else:
+                    colors[:, :3] = 0.7
+            else:
+                colors[:, :3] = 0.7
+            self.loaded.emit(pts, colors, self.filename)
+        except Exception as e:
+            self.failed.emit(str(e))
+
+
+class UltimateDashboard(QMainWindow):
+    def __init__(self):
+        super().__init__()
+
+        self.cfg = load_slam_config()
+        title = self.cfg["app"]["title"]
+        accent = self.cfg["gui"]["colors"]["accent"]
+        maps_dir_cfg = str(self.cfg["app"]["maps_dir"])
+        maps_dir_path = Path(maps_dir_cfg).expanduser()
+        if not maps_dir_path.is_absolute():
+            cfg_path = os.environ.get("SLAM_CONFIG", "").strip()
+            base_dir = Path(cfg_path).expanduser().resolve().parent if cfg_path else Path(__file__).resolve().parent
+            maps_dir_path = (base_dir / maps_dir_path).resolve()
+        maps_dir = str(maps_dir_path)
+
+        self.setWindowTitle(title)
+        self.resize(1600, 900)
+
+        Path(maps_dir).mkdir(parents=True, exist_ok=True)
+
+        self._self_check_popup_shown = False
+        self.client = SlamEngineClient()
+        self.client.start_process()
+        self.show_self_check_popup(self.client.get_self_check(), source="engine")
+
+        self.save_armed = False
+        self.ref_map_path = None
+        self._ref_thread = None
+        self.ref_color_mode = "GRAY"
+        self.density_mode = "MEDIUM"
+        self.min_stable_points = int(self.cfg["map"]["min_points_to_save"])
+        self.loop_closure_enabled = bool(self.cfg.get("loop_closure", {}).get("enabled", False))
+        self.loc_machine_enabled = bool(self.cfg.get("state_machine", {}).get("enabled", True))
+        self.submap_mode_enabled = bool(self.cfg.get("submap_mapping", {}).get("enabled", True))
+        self.autosave_enabled = bool(self.cfg.get("autosave", {}).get("enabled", False))
+        self.autosave_interval_sec = float(self.cfg.get("autosave", {}).get("interval_sec", 90.0))
+        self.nav_cfg = dict(self.cfg.get("navigation_export", {}))
+        self.map_quality_cfg = dict(self.cfg.get("map_quality", {}))
+        self._last_error_popup_text = ""
+        self._last_error_popup_t = 0.0
+        self._error_popup_cooldown_sec = 2.0
+        self._worker_dead_notified = False
+        self._last_saved_popup_text = ""
+        self._last_saved_popup_t = 0.0
+        self._saved_popup_cooldown_sec = 1.0
+        self.current_stable_points = 0
+        self.worker_mode = "IDLE"
+        self.is_connected = False
+        self.recording_enabled = False
+        self.recording_frames = 0
+        self.recording_dropped = 0
+        self.workflow_profile = "BALANCED"
+        self.workflow_selected = False
+        self.last_replay_report = None
+        self.replay_baseline_path = str(Path(maps_dir) / "SLAM_replay_baseline.json")
+        self.approx_pick_armed = False
+        self.approx_guess_z = float(self.cfg.get("localization", {}).get("approx_guess_default_z_m", 0.0))
+        self.last_loc_confidence = 0.0
+        self.last_loc_match_ratio = 0.0
+        self.require_start_pick = True
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._pick_help_shown_for_ref = None
+        self.ref_pick_points = None
+        self.ref_pick_snap_radius_m = float(
+            self.cfg.get("localization", {}).get("pick_snap_radius_m", 1.2)
+        )
+        self.ref_pick_max_points = int(
+            self.cfg.get("localization", {}).get("pick_snap_max_points", 60000)
+        )
+
+        root = QSplitter(Qt.Orientation.Horizontal)
+        self.setCentralWidget(root)
+
+        # Sidebar
+        sidebar = QFrame()
+        sidebar.setMinimumWidth(320)
+        sidebar.setMaximumWidth(760)
+        sidebar.setStyleSheet("background-color: rgba(0,0,0,0.35);")
+        side_layout = QVBoxLayout(sidebar)
+        side_layout.setContentsMargins(18, 18, 18, 18)
+        side_layout.setSpacing(14)
+
+        side_scroll = QScrollArea()
+        side_scroll.setWidgetResizable(True)
+        side_scroll.setFrameShape(QFrame.Shape.NoFrame)
+        side_scroll.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAlwaysOff)
+        side_scroll.setWidget(sidebar)
+        side_scroll.setMinimumWidth(330)
+        side_scroll.setMaximumWidth(780)
+
+        lbl = QLabel("SLAM COMMANDER")
+        lbl.setStyleSheet(f"color:{accent}; font-size:20px; font-weight:900;")
+        side_layout.addWidget(lbl)
+
+        # Network card
+        net_card = QFrame(); net_card.setObjectName("Card"); net_card.setStyleSheet(card_style()); apply_card_shadow(net_card)
+        net_layout = QVBoxLayout(net_card); net_layout.setContentsMargins(14,14,14,14); net_layout.setSpacing(10)
+        net_title = QLabel("NETWORK SETTINGS"); net_title.setObjectName("CardTitle"); net_layout.addWidget(net_title)
+
+        self.txt_iface = QLineEdit(self.cfg["network"]["default_interface"])
+        self.txt_ip = QLineEdit(self.cfg["network"]["default_host_ip"])
+        net_layout.addWidget(self.txt_iface); net_layout.addWidget(self.txt_ip)
+
+        row1 = QHBoxLayout()
+        self.btn_connect = QPushButton("🔌  CONNECT"); self.btn_connect.setStyleSheet(pill_button("#1677ff")); self.btn_connect.clicked.connect(self.on_connect)
+        self.btn_connect.setEnabled(False)
+        self.btn_connect.setToolTip("Choose a workflow first.")
+        self.btn_start = QPushButton("▶  START"); self.btn_start.setStyleSheet(pill_button("#21a453")); self.btn_start.clicked.connect(self.on_start)
+        self.btn_localize_only = QPushButton("🎯  LOCALIZE-ONLY")
+        self.btn_localize_only.setStyleSheet(pill_button("#7a4cff"))
+        self.btn_localize_only.clicked.connect(self.on_start_localize_only)
+        row1.addWidget(self.btn_connect); row1.addWidget(self.btn_start); row1.addWidget(self.btn_localize_only); net_layout.addLayout(row1)
+
+        row2 = QHBoxLayout()
+        self.btn_pause = QPushButton("⏸  PAUSE"); self.btn_pause.setStyleSheet(pill_button("#c78a12", fg="black")); self.btn_pause.clicked.connect(self.on_pause)
+        self.btn_stop = QPushButton("⛔  STOP"); self.btn_stop.setStyleSheet(pill_button("#d0302f")); self.btn_stop.clicked.connect(self.on_stop)
+        row2.addWidget(self.btn_pause); row2.addWidget(self.btn_stop); net_layout.addLayout(row2)
+
+        self.btn_reset = QPushButton("🧹  RESET (NO SAVE)"); self.btn_reset.setStyleSheet(pill_button("#3a3a3a")); self.btn_reset.clicked.connect(self.on_reset)
+        net_layout.addWidget(self.btn_reset)
+
+        side_layout.addWidget(net_card)
+
+        # Workflow card
+        wf_card = QFrame(); wf_card.setObjectName("Card"); wf_card.setStyleSheet(card_style()); apply_card_shadow(wf_card)
+        wf_layout = QVBoxLayout(wf_card); wf_layout.setContentsMargins(14,14,14,14); wf_layout.setSpacing(10)
+        wf_title = QLabel("WORKFLOW (BEFORE RUN)"); wf_title.setObjectName("CardTitle"); wf_layout.addWidget(wf_title)
+
+        self.btn_wf_map_new = QPushButton("🧭 MAP NEW PLACE")
+        self.btn_wf_map_new.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_map_new.clicked.connect(self.on_workflow_map_new)
+        wf_layout.addWidget(self.btn_wf_map_new)
+
+        self.btn_wf_extend = QPushButton("📌 EXTEND SAVED MAP")
+        self.btn_wf_extend.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_extend.clicked.connect(self.on_workflow_extend_map)
+        wf_layout.addWidget(self.btn_wf_extend)
+
+        self.btn_wf_localize = QPushButton("📍 NAVIGATE IN MAPPED PLACE")
+        self.btn_wf_localize.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_localize.clicked.connect(self.on_workflow_localize_existing)
+        wf_layout.addWidget(self.btn_wf_localize)
+
+        self.btn_wf_live_nav = QPushButton("🤖 LIVE NAV WITH MAP")
+        self.btn_wf_live_nav.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_live_nav.clicked.connect(self.on_workflow_live_nav)
+        wf_layout.addWidget(self.btn_wf_live_nav)
+
+        self.btn_wf_live_nav_nomap = QPushButton("🛰 LIVE NAV (NO MAP)")
+        self.btn_wf_live_nav_nomap.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_live_nav_nomap.clicked.connect(self.on_workflow_live_nav_nomap)
+        wf_layout.addWidget(self.btn_wf_live_nav_nomap)
+
+        self.btn_wf_quick = QPushButton("🧪 QUICK DEMO")
+        self.btn_wf_quick.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_wf_quick.clicked.connect(self.on_workflow_quick_demo)
+        wf_layout.addWidget(self.btn_wf_quick)
+
+        self.lbl_workflow = QLabel("Workflow: NOT_SELECTED")
+        self.lbl_workflow.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+        wf_layout.addWidget(self.lbl_workflow)
+
+        side_layout.addWidget(wf_card)
+
+        # Map card
+        map_card = QFrame(); map_card.setObjectName("Card"); map_card.setStyleSheet(card_style()); apply_card_shadow(map_card)
+        map_layout = QVBoxLayout(map_card); map_layout.setContentsMargins(14,14,14,14); map_layout.setSpacing(10)
+        map_title = QLabel("MAP CONTROLS"); map_title.setObjectName("CardTitle"); map_layout.addWidget(map_title)
+
+        self.txt_name = QLineEdit("Work")
+        map_layout.addWidget(self.txt_name)
+
+        self.chk_save = QCheckBox("✅  SAVE ARMED (WILL SAVE ON STOP)")
+        self.chk_save.stateChanged.connect(self.on_toggle_save)
+        map_layout.addWidget(self.chk_save)
+
+        self.btn_save_now = QPushButton("💾  SAVE NOW")
+        self.btn_save_now.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_save_now.clicked.connect(self.on_save_now)
+        map_layout.addWidget(self.btn_save_now)
+
+        auto_title = QLabel("AUTOSAVE")
+        auto_title.setObjectName("CardTitle")
+        map_layout.addWidget(auto_title)
+
+        auto_row = QHBoxLayout()
+        self.chk_autosave = QCheckBox("ON")
+        self.chk_autosave.setChecked(self.autosave_enabled)
+        self.txt_autosave_sec = QLineEdit(str(int(self.autosave_interval_sec)))
+        self.txt_autosave_sec.setMaximumWidth(90)
+        self.btn_apply_autosave = QPushButton("APPLY")
+        self.btn_apply_autosave.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_apply_autosave.clicked.connect(self.on_apply_autosave)
+        auto_row.addWidget(self.chk_autosave)
+        auto_row.addWidget(self.txt_autosave_sec)
+        auto_row.addWidget(self.btn_apply_autosave)
+        map_layout.addLayout(auto_row)
+
+        rec_title = QLabel("RECORD / REPLAY")
+        rec_title.setObjectName("CardTitle")
+        map_layout.addWidget(rec_title)
+
+        rec_row1 = QHBoxLayout()
+        self.btn_record_start = QPushButton("⏺ START REC")
+        self.btn_record_start.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_record_start.clicked.connect(self.on_record_start)
+        self.btn_record_stop = QPushButton("⏹ STOP REC")
+        self.btn_record_stop.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_record_stop.clicked.connect(self.on_record_stop)
+        rec_row1.addWidget(self.btn_record_start)
+        rec_row1.addWidget(self.btn_record_stop)
+        map_layout.addLayout(rec_row1)
+
+        rec_row2 = QHBoxLayout()
+        self.btn_replay_run = QPushButton("▶ RUN REPLAY")
+        self.btn_replay_run.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_replay_run.clicked.connect(self.on_replay_run)
+        self.btn_replay_set_base = QPushButton("📌 SET BASELINE")
+        self.btn_replay_set_base.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_replay_set_base.clicked.connect(self.on_replay_set_baseline)
+        rec_row2.addWidget(self.btn_replay_run)
+        rec_row2.addWidget(self.btn_replay_set_base)
+        map_layout.addLayout(rec_row2)
+
+        min_pts_title = QLabel("MIN STABLE POINTS")
+        min_pts_title.setObjectName("CardTitle")
+        map_layout.addWidget(min_pts_title)
+
+        min_pts_row = QHBoxLayout()
+        self.txt_min_stable = QLineEdit(str(self.min_stable_points))
+        self.txt_min_stable.returnPressed.connect(self.on_apply_min_stable_points)
+        self.btn_apply_min_stable = QPushButton("APPLY")
+        self.btn_apply_min_stable.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_apply_min_stable.clicked.connect(self.on_apply_min_stable_points)
+        min_pts_row.addWidget(self.txt_min_stable)
+        min_pts_row.addWidget(self.btn_apply_min_stable)
+        map_layout.addLayout(min_pts_row)
+
+        self.btn_export_nav = QPushButton("🧭  EXPORT NAV")
+        self.btn_export_nav.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_export_nav.clicked.connect(self.on_export_nav)
+        map_layout.addWidget(self.btn_export_nav)
+
+        nav_title = QLabel("NAV TUNING")
+        nav_title.setObjectName("CardTitle")
+        map_layout.addWidget(nav_title)
+
+        nav_row1 = QHBoxLayout()
+        self.txt_nav_zmin = QLineEdit(str(self.nav_cfg.get("z_min_m", -0.4)))
+        self.txt_nav_zmax = QLineEdit(str(self.nav_cfg.get("z_max_m", 1.2)))
+        self.txt_nav_zmin.setPlaceholderText("z min")
+        self.txt_nav_zmax.setPlaceholderText("z max")
+        nav_row1.addWidget(self.txt_nav_zmin)
+        nav_row1.addWidget(self.txt_nav_zmax)
+        map_layout.addLayout(nav_row1)
+
+        nav_row2 = QHBoxLayout()
+        self.txt_nav_res = QLineEdit(str(self.nav_cfg.get("resolution_m", 0.05)))
+        self.txt_nav_inf = QLineEdit(str(self.nav_cfg.get("inflation_radius_m", 0.2)))
+        self.txt_nav_res.setPlaceholderText("res")
+        self.txt_nav_inf.setPlaceholderText("inflate")
+        self.btn_apply_nav = QPushButton("APPLY NAV")
+        self.btn_apply_nav.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_apply_nav.clicked.connect(self.on_apply_nav_config)
+        nav_row2.addWidget(self.txt_nav_res)
+        nav_row2.addWidget(self.txt_nav_inf)
+        nav_row2.addWidget(self.btn_apply_nav)
+        map_layout.addLayout(nav_row2)
+
+        goal_row = QHBoxLayout()
+        self.txt_goal_x = QLineEdit("0.0")
+        self.txt_goal_y = QLineEdit("0.0")
+        self.txt_goal_x.setPlaceholderText("goal x")
+        self.txt_goal_y.setPlaceholderText("goal y")
+        self.btn_goal_apply = QPushButton("SET GOAL")
+        self.btn_goal_apply.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_goal_apply.clicked.connect(self.on_set_nav_goal)
+        self.btn_goal_clear = QPushButton("CLEAR")
+        self.btn_goal_clear.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_goal_clear.clicked.connect(self.on_clear_nav_goal)
+        goal_row.addWidget(self.txt_goal_x)
+        goal_row.addWidget(self.txt_goal_y)
+        goal_row.addWidget(self.btn_goal_apply)
+        goal_row.addWidget(self.btn_goal_clear)
+        map_layout.addLayout(goal_row)
+
+        mission_row1 = QHBoxLayout()
+        self.txt_mission = QLineEdit("1.0,0.0; 2.0,0.0")
+        self.txt_mission.setPlaceholderText("x,y; x,y; ...")
+        self.btn_mission_start = QPushButton("MISSION START")
+        self.btn_mission_start.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_mission_start.clicked.connect(self.on_mission_start)
+        mission_row1.addWidget(self.txt_mission)
+        mission_row1.addWidget(self.btn_mission_start)
+        map_layout.addLayout(mission_row1)
+
+        mission_row2 = QHBoxLayout()
+        self.btn_mission_pause = QPushButton("PAUSE M")
+        self.btn_mission_pause.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_mission_pause.clicked.connect(lambda: self._send_mission_cmd("pause"))
+        self.btn_mission_resume = QPushButton("RESUME M")
+        self.btn_mission_resume.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_mission_resume.clicked.connect(lambda: self._send_mission_cmd("resume"))
+        self.btn_mission_stop = QPushButton("STOP M")
+        self.btn_mission_stop.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_mission_stop.clicked.connect(lambda: self._send_mission_cmd("stop"))
+        mission_row2.addWidget(self.btn_mission_pause)
+        mission_row2.addWidget(self.btn_mission_resume)
+        mission_row2.addWidget(self.btn_mission_stop)
+        map_layout.addLayout(mission_row2)
+
+        den_title = QLabel("POINT DENSITY")
+        den_title.setObjectName("CardTitle")
+        map_layout.addWidget(den_title)
+
+        den_row = QHBoxLayout()
+        self.btn_den_low = QPushButton("LOW")
+        self.btn_den_med = QPushButton("MEDIUM")
+        self.btn_den_high = QPushButton("HIGH")
+        self.btn_den_low.clicked.connect(lambda: self.set_density_mode("LOW"))
+        self.btn_den_med.clicked.connect(lambda: self.set_density_mode("MEDIUM"))
+        self.btn_den_high.clicked.connect(lambda: self.set_density_mode("HIGH"))
+        den_row.addWidget(self.btn_den_low)
+        den_row.addWidget(self.btn_den_med)
+        den_row.addWidget(self.btn_den_high)
+        map_layout.addLayout(den_row)
+        self.refresh_density_buttons()
+
+        self.chk_loop_closure = QCheckBox("🔁 LOOP CLOSURE SAFE")
+        self.chk_loop_closure.setChecked(self.loop_closure_enabled)
+        self.chk_loop_closure.stateChanged.connect(self.on_toggle_loop_closure)
+        map_layout.addWidget(self.chk_loop_closure)
+
+        self.chk_loc_machine = QCheckBox("🧠 LOC STATE MACHINE")
+        self.chk_loc_machine.setChecked(self.loc_machine_enabled)
+        self.chk_loc_machine.stateChanged.connect(self.on_toggle_loc_machine)
+        map_layout.addWidget(self.chk_loc_machine)
+
+        self.chk_submap_mode = QCheckBox("🗺️ SUBMAP (LOCAL+GLOBAL)")
+        self.chk_submap_mode.setChecked(self.submap_mode_enabled)
+        self.chk_submap_mode.stateChanged.connect(self.on_toggle_submap_mode)
+        map_layout.addWidget(self.chk_submap_mode)
+
+        quality_title = QLabel("MAP QUALITY")
+        quality_title.setObjectName("CardTitle")
+        map_layout.addWidget(quality_title)
+
+        q_row1 = QHBoxLayout()
+        self.txt_near_range = QLineEdit(str(self.map_quality_cfg.get("near_min_range_m", 0.15)))
+        self.chk_outlier = QCheckBox("Outlier")
+        self.chk_outlier.setChecked(bool(self.map_quality_cfg.get("outlier_filter_enabled", False)))
+        q_row1.addWidget(self.txt_near_range)
+        q_row1.addWidget(self.chk_outlier)
+        map_layout.addLayout(q_row1)
+
+        q_row2 = QHBoxLayout()
+        self.chk_clip_z = QCheckBox("Clip Z")
+        self.chk_clip_z.setChecked(bool(self.map_quality_cfg.get("world_z_clip_enabled", False)))
+        self.txt_world_zmin = QLineEdit(str(self.map_quality_cfg.get("world_z_min_m", -2.0)))
+        self.txt_world_zmax = QLineEdit(str(self.map_quality_cfg.get("world_z_max_m", 3.0)))
+        q_row2.addWidget(self.chk_clip_z)
+        q_row2.addWidget(self.txt_world_zmin)
+        q_row2.addWidget(self.txt_world_zmax)
+        map_layout.addLayout(q_row2)
+
+        q_row3 = QHBoxLayout()
+        self.txt_outlier_voxel = QLineEdit(str(self.map_quality_cfg.get("outlier_voxel_m", 0.12)))
+        self.txt_outlier_min = QLineEdit(str(self.map_quality_cfg.get("outlier_min_points", 2)))
+        self.btn_apply_quality = QPushButton("APPLY Q")
+        self.btn_apply_quality.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_apply_quality.clicked.connect(self.on_apply_map_quality)
+        q_row3.addWidget(self.txt_outlier_voxel)
+        q_row3.addWidget(self.txt_outlier_min)
+        q_row3.addWidget(self.btn_apply_quality)
+        map_layout.addLayout(q_row3)
+
+        side_layout.addWidget(map_card)
+
+        # Localization card
+        loc_card = QFrame(); loc_card.setObjectName("Card"); loc_card.setStyleSheet(card_style()); apply_card_shadow(loc_card)
+        loc_layout = QVBoxLayout(loc_card); loc_layout.setContentsMargins(14,14,14,14); loc_layout.setSpacing(10)
+        loc_title = QLabel("LOCALIZATION"); loc_title.setObjectName("CardTitle"); loc_layout.addWidget(loc_title)
+
+        rowl = QHBoxLayout()
+        self.btn_load_ref = QPushButton("🗺️  LOAD REF MAP"); self.btn_load_ref.setStyleSheet(pill_button("#2b2b2b")); self.btn_load_ref.clicked.connect(self.on_load_ref)
+        self.btn_localize = QPushButton("📍  LOCALIZE NOW"); self.btn_localize.setStyleSheet(pill_button("#2b2b2b")); self.btn_localize.clicked.connect(self.on_localize)
+        rowl.addWidget(self.btn_load_ref); rowl.addWidget(self.btn_localize)
+        loc_layout.addLayout(rowl)
+
+        self.btn_clear_ref = QPushButton("🧽  CLEAR REF"); self.btn_clear_ref.setStyleSheet(pill_button("#2b2b2b")); self.btn_clear_ref.clicked.connect(self.on_clear_ref)
+        loc_layout.addWidget(self.btn_clear_ref)
+
+        ref_color_title = QLabel("REF MAP COLOR")
+        ref_color_title.setObjectName("CardTitle")
+        loc_layout.addWidget(ref_color_title)
+
+        ref_color_row = QHBoxLayout()
+        self.btn_ref_gray = QPushButton("GRAY")
+        self.btn_ref_height = QPushButton("HEIGHT")
+        self.btn_ref_orig = QPushButton("ORIGINAL")
+        self.btn_ref_gray.clicked.connect(lambda: self.set_ref_color_mode("GRAY"))
+        self.btn_ref_height.clicked.connect(lambda: self.set_ref_color_mode("HEIGHT"))
+        self.btn_ref_orig.clicked.connect(lambda: self.set_ref_color_mode("ORIGINAL"))
+        ref_color_row.addWidget(self.btn_ref_gray)
+        ref_color_row.addWidget(self.btn_ref_height)
+        ref_color_row.addWidget(self.btn_ref_orig)
+        loc_layout.addLayout(ref_color_row)
+        self.refresh_ref_color_buttons()
+
+        approx_title = QLabel("START POINTER")
+        approx_title.setObjectName("CardTitle")
+        loc_layout.addWidget(approx_title)
+
+        approx_row1 = QHBoxLayout()
+        self.txt_approx_z = QLineEdit(f"{self.approx_guess_z:.2f}")
+        self.txt_approx_z.setPlaceholderText("z")
+        self.txt_approx_z.setVisible(False)
+        self.btn_pick_approx = QPushButton("🧭 CLICK START POINTER")
+        self.btn_pick_approx.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_pick_approx.clicked.connect(self.on_set_approx_pick)
+        approx_row1.addWidget(self.btn_pick_approx)
+        loc_layout.addLayout(approx_row1)
+
+        self.btn_clear_approx = QPushButton("✖ CLEAR APPROX")
+        self.btn_clear_approx.setStyleSheet(pill_button("#2b2b2b"))
+        self.btn_clear_approx.clicked.connect(self.on_clear_approx)
+        loc_layout.addWidget(self.btn_clear_approx)
+
+        self.chk_require_start_pick = QCheckBox("Require start-point pick before START")
+        self.chk_require_start_pick.setChecked(True)
+        self.chk_require_start_pick.stateChanged.connect(self.on_toggle_require_start_pick)
+        loc_layout.addWidget(self.chk_require_start_pick)
+
+        self.lbl_pick_status = QLabel("Start pick: pending")
+        self.lbl_pick_status.setStyleSheet("color:#ffcc00; font-family:monospace; font-weight:800;")
+        loc_layout.addWidget(self.lbl_pick_status)
+
+        side_layout.addWidget(loc_card)
+
+        perf_card = QFrame(); perf_card.setObjectName("Card"); perf_card.setStyleSheet(card_style()); apply_card_shadow(perf_card)
+        perf_layout = QVBoxLayout(perf_card); perf_layout.setContentsMargins(14,14,14,14); perf_layout.setSpacing(6)
+        perf_title = QLabel("PERFORMANCE"); perf_title.setObjectName("CardTitle"); perf_layout.addWidget(perf_title)
+        self.lbl_perf_fps = QLabel("FPS in/out: 0.0 / 0.0")
+        self.lbl_perf_fps.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        self.lbl_perf_icp = QLabel("ICP: 0.0 ms  CPU: 0.0%")
+        self.lbl_perf_icp.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        self.lbl_perf_queue = QLabel("Queue lag: 0  Growth: 0.0 pts/s")
+        self.lbl_perf_queue.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        self.lbl_mode = QLabel("Mode: IDLE  Rec: OFF")
+        self.lbl_mode.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        self.lbl_nav = QLabel("Nav: goal none  cmd 0.00/0.00")
+        self.lbl_nav.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        self.lbl_loc_match = QLabel("Match: n/a")
+        self.lbl_loc_match.setStyleSheet("color:white; font-family:monospace; font-weight:700;")
+        perf_layout.addWidget(self.lbl_perf_fps)
+        perf_layout.addWidget(self.lbl_perf_icp)
+        perf_layout.addWidget(self.lbl_perf_queue)
+        perf_layout.addWidget(self.lbl_mode)
+        perf_layout.addWidget(self.lbl_nav)
+        perf_layout.addWidget(self.lbl_loc_match)
+        side_layout.addWidget(perf_card)
+
+        side_layout.addStretch(1)
+
+        # Status labels
+        self.lbl_status = QLabel("Status: IDLE")
+        self.lbl_status.setStyleSheet(f"color:{accent}; font-family:monospace; font-weight:900;")
+
+        self.lbl_points = QLabel("Stable points: 0")
+        self.lbl_points.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        self.lbl_pose = QLabel("Pos: 0.00, 0.00, 0.00")
+        self.lbl_pose.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        self.lbl_loc = QLabel("Localization: NO_REF")
+        self.lbl_loc.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        self.lbl_loc_state = QLabel("Loc health: TRACKING")
+        self.lbl_loc_state.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        self.lbl_density = QLabel(f"Density: {self.density_mode}")
+        self.lbl_density.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        self.lbl_min_stable = QLabel(f"Min stable: {self.min_stable_points}")
+        self.lbl_min_stable.setStyleSheet("color:white; font-family:monospace; font-weight:800;")
+
+        side_layout.addWidget(self.lbl_status)
+        side_layout.addWidget(self.lbl_points)
+        side_layout.addWidget(self.lbl_pose)
+        side_layout.addWidget(self.lbl_loc)
+        side_layout.addWidget(self.lbl_loc_state)
+        side_layout.addWidget(self.lbl_density)
+        side_layout.addWidget(self.lbl_min_stable)
+
+        root.addWidget(side_scroll)
+
+        # 3D view
+        self.view = SLAMGLViewWidget()
+        self.view.opts["distance"] = float(self.cfg["gui"]["view"]["distance"])
+        self.view.setBackgroundColor("#0a0c10")
+        root.addWidget(self.view)
+        root.setChildrenCollapsible(False)
+        root.setHandleWidth(10)
+        root.setStretchFactor(0, 0)
+        root.setStretchFactor(1, 1)
+        root.setSizes([380, 1220])
+
+        grid = gl.GLGridItem()
+        grid.setSize(x=float(self.cfg["gui"]["view"]["grid_size"]), y=float(self.cfg["gui"]["view"]["grid_size"]), z=0)
+        self.view.addItem(grid)
+
+        self.axis = gl.GLAxisItem()
+        self.axis.setSize(2,2,2)
+        self.view.addItem(self.axis)
+
+        self.scatter = gl.GLScatterPlotItem(pos=np.zeros((1,3),dtype=np.float32), size=2)
+        self.scatter.setGLOptions("opaque")
+        self.view.addItem(self.scatter)
+
+        # Reference map overlay (shown after user loads a map)
+        self.ref_scatter = gl.GLScatterPlotItem(pos=np.zeros((1,3),dtype=np.float32), size=1.2)
+        self.ref_scatter.setGLOptions("translucent")
+        self.view.addItem(self.ref_scatter)
+        self.ref_match_scatter = gl.GLScatterPlotItem(pos=np.zeros((1,3),dtype=np.float32), size=1.4)
+        self.ref_match_scatter.setGLOptions("translucent")
+        self.view.addItem(self.ref_match_scatter)
+        self.start_ptr_scatter = gl.GLScatterPlotItem(pos=np.zeros((1,3),dtype=np.float32), size=1.8)
+        self.start_ptr_scatter.setGLOptions("translucent")
+        self.view.addItem(self.start_ptr_scatter)
+        self.view.mapPickDrag.connect(self.on_view_map_pick_drag)
+
+        self.timer = QTimer()
+        self.timer.timeout.connect(self.update_loop)
+        self.timer.start(50)
+
+        self.max_draw = int(self.cfg["gui"]["render"]["max_draw_points"])
+        self.max_ref_draw = max(50000, self.max_draw // 2)
+        self.decimate_step = int(self.cfg["gui"]["render"]["decimate_step"])
+        self.maps_dir = maps_dir
+        self.ref_display_voxel = float(self.cfg["localization"]["ref_display_voxel"])
+
+        # Workflow UI control groups: non-relevant controls are disabled/greyed.
+        self._workflow_widget_groups = {
+            "runtime": [
+                self.txt_min_stable, self.btn_apply_min_stable,
+                self.btn_den_low, self.btn_den_med, self.btn_den_high,
+            ],
+            "mapping": [
+                self.txt_name, self.chk_save, self.btn_save_now,
+                self.chk_autosave, self.txt_autosave_sec, self.btn_apply_autosave,
+                self.chk_loop_closure,
+                self.txt_near_range, self.chk_outlier, self.chk_clip_z,
+                self.txt_world_zmin, self.txt_world_zmax,
+                self.txt_outlier_voxel, self.txt_outlier_min, self.btn_apply_quality,
+            ],
+            "recording": [
+                self.btn_record_start, self.btn_record_stop,
+                self.btn_replay_run, self.btn_replay_set_base,
+            ],
+            "localization": [
+                self.btn_localize_only, self.btn_load_ref, self.btn_localize, self.btn_clear_ref,
+                self.chk_loc_machine,
+                self.btn_ref_gray, self.btn_ref_height, self.btn_ref_orig,
+                self.txt_approx_z, self.btn_pick_approx, self.btn_clear_approx,
+                self.chk_require_start_pick, self.lbl_pick_status,
+            ],
+            "submap_nav": [
+                self.chk_submap_mode,
+            ],
+            "nav_export": [
+                self.btn_export_nav, self.txt_nav_zmin, self.txt_nav_zmax,
+                self.txt_nav_res, self.txt_nav_inf, self.btn_apply_nav,
+            ],
+            "nav_live": [
+                self.txt_goal_x, self.txt_goal_y, self.btn_goal_apply, self.btn_goal_clear,
+                self.txt_mission, self.btn_mission_start,
+                self.btn_mission_pause, self.btn_mission_resume, self.btn_mission_stop,
+            ],
+        }
+        self._workflow_managed_widgets = []
+        seen = set()
+        for group_widgets in self._workflow_widget_groups.values():
+            for widget in group_widgets:
+                k = id(widget)
+                if k in seen:
+                    continue
+                seen.add(k)
+                self._workflow_managed_widgets.append(widget)
+        self._apply_workflow_ui_rules(self.workflow_profile, update_label=True)
+        self._refresh_pick_status()
+        self._hide_start_pointer_visual()
+
+    def set_status(self, text: str, color: str):
+        self.lbl_status.setText(text)
+        self.lbl_status.setStyleSheet(f"color:{color}; font-family:monospace; font-weight:900;")
+
+    def show_error_popup(self, message: str):
+        msg = str(message).strip() or "Unknown error"
+        now = time.monotonic()
+        if msg == self._last_error_popup_text and (now - self._last_error_popup_t) < self._error_popup_cooldown_sec:
+            return
+        self._last_error_popup_text = msg
+        self._last_error_popup_t = now
+        QMessageBox.critical(self, "SLAM Error", msg)
+
+    def show_saved_popup(self, message: str):
+        msg = str(message).strip()
+        if not msg:
+            return
+        now = time.monotonic()
+        if msg == self._last_saved_popup_text and (now - self._last_saved_popup_t) < self._saved_popup_cooldown_sec:
+            return
+        self._last_saved_popup_text = msg
+        self._last_saved_popup_t = now
+        QMessageBox.information(self, "Map Saved", msg)
+
+    def show_self_check_popup(self, report: dict | None, source: str = "startup"):
+        if self._self_check_popup_shown and source != "worker":
+            return
+        rep = report or {}
+        errs = list(rep.get("errors", []) or [])
+        warns = list(rep.get("warnings", []) or [])
+        info = list(rep.get("info", []) or [])
+        if not errs and not warns:
+            if source == "engine":
+                self._self_check_popup_shown = True
+            return
+        msg_lines = []
+        if errs:
+            msg_lines.append("Errors:")
+            msg_lines.extend(f"- {x}" for x in errs)
+        if warns:
+            msg_lines.append("Warnings:")
+            msg_lines.extend(f"- {x}" for x in warns)
+        if info:
+            msg_lines.append("Info:")
+            msg_lines.extend(f"- {x}" for x in info[:3])
+        text = "\n".join(msg_lines)
+        if errs:
+            QMessageBox.critical(self, "SLAM Startup Check", text)
+        else:
+            QMessageBox.warning(self, "SLAM Startup Check", text)
+        if source == "engine":
+            self._self_check_popup_shown = True
+
+    @staticmethod
+    def _read_float(line: QLineEdit, default: float) -> float:
+        txt = line.text().strip()
+        try:
+            return float(txt)
+        except Exception:
+            line.setText(str(default))
+            return float(default)
+
+    def request_map_export_if_armed(self) -> bool:
+        if not self.save_armed:
+            return False
+        base = self.txt_name.text().strip() or "map_robot"
+        self.client.export_map(base)
+        return True
+
+    def _read_min_stable_input(self, show_error: bool = False):
+        txt = self.txt_min_stable.text().strip()
+        try:
+            value = int(txt)
+        except Exception:
+            if show_error:
+                self.show_error_popup("Minimum stable points must be an integer.")
+            return None
+        return int(np.clip(value, 50, 5_000_000))
+
+    def _apply_min_stable_value(self, value: int, set_status_msg: bool = True):
+        self.min_stable_points = int(value)
+        self.txt_min_stable.setText(str(self.min_stable_points))
+        self.lbl_min_stable.setText(f"Min stable: {self.min_stable_points}")
+        try:
+            self.client.set_min_stable_points(self.min_stable_points)
+        except Exception:
+            pass
+        if set_status_msg:
+            self.set_status(f"Min stable set: {self.min_stable_points}", "#ffcc00")
+
+    def ensure_min_stable_synced(self, show_error: bool = False, set_status_msg: bool = False) -> bool:
+        value = self._read_min_stable_input(show_error=show_error)
+        if value is None:
+            return False
+        if int(value) != int(self.min_stable_points):
+            self._apply_min_stable_value(int(value), set_status_msg=set_status_msg)
+        return True
+
+    def _set_widgets_enabled(self, widgets, enabled: bool):
+        for w in widgets:
+            if w is None:
+                continue
+            w.setEnabled(bool(enabled))
+
+    def _apply_workflow_ui_rules(self, profile: str | None = None, update_label: bool = False):
+        p = str(profile if profile is not None else self.workflow_profile).upper().strip()
+        if p not in ("MAP_NEW", "EXTEND_MAP", "LOCALIZE_MAP", "LIVE_NAV_MAP", "LIVE_NAV_NO_MAP", "QUICK_DEMO", "BALANCED"):
+            p = "BALANCED"
+        self.workflow_profile = p
+        if update_label:
+            if self.workflow_selected:
+                self.lbl_workflow.setText(f"Workflow: {self.workflow_profile}")
+            else:
+                self.lbl_workflow.setText("Workflow: NOT_SELECTED")
+
+        # Start from fully enabled, then gray-out non-relevant controls per workflow.
+        self._set_widgets_enabled(self._workflow_managed_widgets, True)
+        # Submap LOCAL+GLOBAL is intentionally limited to map-based localization/nav workflows.
+        self._set_widgets_enabled(
+            self._workflow_widget_groups["submap_nav"],
+            p in ("LOCALIZE_MAP", "LIVE_NAV_MAP"),
+        )
+
+        if p == "MAP_NEW":
+            self._set_widgets_enabled(self._workflow_widget_groups["localization"], False)
+            self._set_widgets_enabled(self._workflow_widget_groups["nav_live"], False)
+        elif p == "EXTEND_MAP":
+            # Extending an existing map needs localization (to anchor incoming
+            # scans to the loaded frame) AND mapping (to add the new points).
+            self._set_widgets_enabled(self._workflow_widget_groups["nav_live"], False)
+        elif p == "LOCALIZE_MAP":
+            self._set_widgets_enabled(self._workflow_widget_groups["mapping"], False)
+            self._set_widgets_enabled(self._workflow_widget_groups["recording"], False)
+            self._set_widgets_enabled(self._workflow_widget_groups["nav_export"], False)
+            self._set_widgets_enabled(self._workflow_widget_groups["nav_live"], False)
+        elif p == "LIVE_NAV_MAP":
+            self._set_widgets_enabled(self._workflow_widget_groups["mapping"], False)
+            self._set_widgets_enabled(self._workflow_widget_groups["recording"], False)
+        elif p == "LIVE_NAV_NO_MAP":
+            self._set_widgets_enabled(self._workflow_widget_groups["localization"], False)
+        elif p == "QUICK_DEMO":
+            # Quick demo keeps most options active, but submap remains map-workflow only.
+            pass
+        else:
+            # BALANCED keeps broad controls active; submap remains map-workflow only.
+            pass
+        if p not in ("LOCALIZE_MAP", "LIVE_NAV_MAP") and self.approx_pick_armed:
+            self._set_approx_pick_mode(False)
+        self._refresh_pick_status()
+
+    def apply_runtime_settings(self):
+        try:
+            self.client.set_density(self.density_mode)
+        except Exception:
+            pass
+        if not self.ensure_min_stable_synced(show_error=False, set_status_msg=False):
+            try:
+                self.client.set_min_stable_points(self.min_stable_points)
+            except Exception:
+                pass
+        try:
+            self.client.set_loop_closure(bool(self.loop_closure_enabled))
+        except Exception:
+            pass
+        try:
+            self.client.set_loc_state_machine(bool(self.loc_machine_enabled))
+        except Exception:
+            pass
+        try:
+            self.client.set_submap_mode(
+                bool(self.submap_mode_enabled),
+                {"apply_profiles": ["LOCALIZE_MAP", "LIVE_NAV_MAP"]},
+            )
+        except Exception:
+            pass
+        try:
+            self.client.set_stability_profile(str(self.workflow_profile))
+        except Exception:
+            pass
+        self.on_apply_autosave(set_status_msg=False, quiet=True)
+        self.on_apply_nav_config(set_status_msg=False, quiet=True)
+        self.on_apply_map_quality(set_status_msg=False, quiet=True)
+
+    def _apply_workflow_profile(self, profile: str, set_status_msg: bool = True):
+        p = str(profile).upper().strip()
+        self.workflow_selected = True
+        self._apply_workflow_ui_rules(p, update_label=True)
+        self.btn_connect.setEnabled(True)
+        self.btn_connect.setToolTip("")
+        try:
+            self.client.set_stability_profile(self.workflow_profile)
+        except Exception:
+            pass
+        if set_status_msg:
+            self.set_status(f"Workflow selected: {self.workflow_profile}", "#ffcc00")
+
+    def _pick_and_load_ref_map(self) -> bool:
+        fname, _ = QFileDialog.getOpenFileName(self, "Load Reference Map", self.maps_dir, "Point Clouds (*.ply *.pcd)")
+        if not fname:
+            return False
+        self.ref_map_path = fname
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._pick_help_shown_for_ref = None
+        self._refresh_pick_status()
+        self.client.load_ref_map(fname)
+        self.lbl_loc.setText(f"Localization: ref={Path(fname).name}")
+        self._start_ref_loader(fname, show_status=False)
+        return True
+
+    def _workflow_requires_ref_map(self) -> bool:
+        return str(self.workflow_profile).upper().strip() in ("LOCALIZE_MAP", "LIVE_NAV_MAP")
+
+    def on_workflow_map_new(self):
+        self._apply_workflow_profile("MAP_NEW", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("MEDIUM")
+        # Keep loop closure off by default for stability; user can enable manually later.
+        self.chk_loop_closure.setChecked(False)
+        self.chk_submap_mode.setChecked(False)
+        # Mapping-new should run in pure SLAM frame (no stale reference alignment).
+        if self.ref_map_path:
+            self.on_clear_ref()
+        self.set_status("Workflow MAP_NEW ready: CONNECT -> START mapping (loop closure OFF by default).", "#ffcc00")
+
+    def on_workflow_extend_map(self):
+        """Extend an existing saved map: load .ply, seed stable map, continue mapping."""
+        fname, _ = QFileDialog.getOpenFileName(
+            self, "Extend Saved Map", self.maps_dir, "Point Clouds (*.ply *.pcd)"
+        )
+        if not fname:
+            return
+        self._apply_workflow_profile("EXTEND_MAP", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("MEDIUM")
+        # Loop closure on by default for extend — drift accumulated during the
+        # original scan plus drift in the new segment both benefit from it.
+        self.chk_loop_closure.setChecked(True)
+        self.chk_submap_mode.setChecked(False)
+        # Wire ref_map_path so the GUI shows the loaded map in the viewer.
+        self.ref_map_path = fname
+        self.lbl_loc.setText(f"Localization: ref={Path(fname).name}")
+        self._start_ref_loader(fname, show_status=False)
+        # Send the extend command (also sets ref internally and seeds stable/filter).
+        try:
+            self.client.load_for_extend(fname)
+        except Exception as e:
+            self.show_error_popup(f"Extend load failed: {e}")
+            return
+        self.set_status(
+            f"Workflow EXTEND_MAP ready (loaded {Path(fname).name}): CONNECT -> START.",
+            "#ffcc00",
+        )
+        QMessageBox.information(
+            self,
+            "Extend Saved Map",
+            "1) Click CONNECT\n2) Click START\n\n"
+            "New scans will be added to the loaded map. Save as usual on STOP — "
+            "the merged map is written to a new file (auto-incremented name).",
+        )
+
+    def on_workflow_localize_existing(self):
+        self._apply_workflow_profile("LOCALIZE_MAP", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("MEDIUM")
+        self.chk_loop_closure.setChecked(False)
+        self.chk_loc_machine.setChecked(True)
+        # Reference-anchored default: keep uploaded map static, use submap only if user enables it.
+        self.chk_submap_mode.setChecked(False)
+        self.set_status(
+            "Workflow LOCALIZE_MAP ready: LOAD REF -> click start pointer -> CONNECT -> START.",
+            "#ffcc00",
+        )
+        QMessageBox.information(
+            self,
+            "Navigate In Mapped Place",
+            "1) Click LOAD REF MAP\n2) Click CLICK START POINTER, then click current location on map\n3) Click CONNECT\n4) Click START\n\nLocalization will start automatically from your picked start location.",
+        )
+
+    def on_workflow_live_nav(self):
+        self._apply_workflow_profile("LIVE_NAV_MAP", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("HIGH")
+        self.chk_loop_closure.setChecked(False)
+        self.chk_loc_machine.setChecked(True)
+        # Reference-anchored default: keep uploaded map static, use submap only if user enables it.
+        self.chk_submap_mode.setChecked(False)
+        try:
+            self.client.set_nav_runtime_cfg({"enabled": True, "dynamic_decay_sec": 1.6, "dynamic_min_hits": 2})
+        except Exception:
+            pass
+        self.set_status(
+            "Workflow LIVE_NAV_MAP ready: LOAD REF -> click start pointer -> CONNECT -> START.",
+            "#ffcc00",
+        )
+        QMessageBox.information(
+            self,
+            "Live Nav With Map",
+            "1) Click LOAD REF MAP\n2) Click CLICK START POINTER, then click current location on map\n3) Click CONNECT\n4) Click START\n\nLocalization will start automatically from your picked start location.",
+        )
+
+    def on_workflow_live_nav_nomap(self):
+        self._apply_workflow_profile("LIVE_NAV_NO_MAP", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("HIGH")
+        self.chk_loop_closure.setChecked(False)
+        self.chk_submap_mode.setChecked(False)
+        if self.ref_map_path:
+            # Force no-reference mode for live mapless navigation.
+            self.on_clear_ref()
+        try:
+            self.client.set_nav_runtime_cfg({"enabled": True, "dynamic_decay_sec": 1.4, "dynamic_min_hits": 2})
+        except Exception:
+            pass
+        self.set_status("Workflow LIVE_NAV_NO_MAP ready: CONNECT -> START -> set GOAL/MISSION.", "#ffcc00")
+
+    def on_workflow_quick_demo(self):
+        self._apply_workflow_profile("QUICK_DEMO", set_status_msg=False)
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._refresh_pick_status()
+        self.set_density_mode("HIGH")
+        self.set_status("Workflow QUICK_DEMO ready (fast build, lower persistence).", "#ffcc00")
+
+    # actions
+    def on_connect(self):
+        if not self.workflow_selected:
+            self.show_error_popup("Select a workflow first, then CONNECT.")
+            self.set_status("Status: select workflow first", "#ffcc00")
+            return
+        if self._workflow_requires_ref_map() and not self.ref_map_path:
+            self.show_error_popup("Load a reference map first for this workflow.")
+            self.set_status("Status: load ref map first", "#ffcc00")
+            return
+        # Update config-derived IP at runtime by environment is not needed; just re-create engine process
+        ip = self.txt_ip.text().strip()
+        if not ip:
+            self.set_status("Status: IP required", "#ffcc00")
+            return
+
+        # write into process config by setting env and recreating client config file? keep behavior: just connect
+        # engine reads config ip; for runtime typed IP, we pass command CONNECT and worker uses eng_cfg.host_ip.
+        # easiest: set env override and restart client
+        self.client.stop_process()
+        # update env config override is not a numeric param; it’s user input.
+        # We rebuild a new client, then mutate host_ip for this session:
+        self.client = SlamEngineClient()
+        self.client.eng_cfg.host_ip = ip
+        self.client.start_process()
+        self.show_self_check_popup(self.client.get_self_check(), source="engine")
+        self._worker_dead_notified = False
+        self.worker_mode = "IDLE"
+        self.is_connected = False
+        self.recording_enabled = False
+        self.recording_frames = 0
+        self.recording_dropped = 0
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+        self.client.connect()
+        self.apply_runtime_settings()
+        if self._workflow_requires_ref_map() and self.ref_map_path:
+            self.client.load_ref_map(self.ref_map_path)
+            if self.start_pick_ready and isinstance(self.start_pick_xyz, tuple) and len(self.start_pick_xyz) == 3:
+                px, py, pz = self.start_pick_xyz
+                try:
+                    self.client.set_approx_pose(float(px), float(py), float(pz))
+                except Exception:
+                    pass
+            else:
+                self._prompt_pick_before_start_if_needed()
+        self.set_status("Status: CONNECTING...", self.cfg["gui"]["colors"]["accent"])
+
+    def on_start(self):
+        if not self.is_connected:
+            self.show_error_popup("Click CONNECT first.")
+            return
+        self._set_approx_pick_mode(False)
+        self.apply_runtime_settings()
+        if self._workflow_requires_ref_map():
+            if not self.ref_map_path:
+                self.show_error_popup("Load a reference map first, then CONNECT and START.")
+                self.set_status("Status: load ref map first", "#ffcc00")
+                return
+            if self.require_start_pick and not self.start_pick_ready:
+                self.show_error_popup("Set approximate start location on the map first.")
+                self._prompt_pick_before_start_if_needed()
+                self.set_status("Status: click map to set start location", "#ffcc00")
+                return
+            if self.start_pick_ready and isinstance(self.start_pick_xyz, tuple) and len(self.start_pick_xyz) == 3:
+                px, py, pz = self.start_pick_xyz
+                try:
+                    self.client.set_approx_pose(float(px), float(py), float(pz))
+                except Exception:
+                    pass
+            self.client.start_localize_only()
+            self.worker_mode = "LOCALIZE_ONLY"
+            self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+            self.set_status("Status: LOCALIZE_ONLY (AUTO LOCALIZING FROM LIVE SCAN)", "#ffcc00")
+            return
+        self.client.start_mapping()
+        self.worker_mode = "MAPPING"
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+        self.set_status("Status: MAPPING", self.cfg["gui"]["colors"]["accent"])
+
+    def on_start_localize_only(self):
+        if not self.ref_map_path:
+            self.show_error_popup("Load a reference map first for LOCALIZE-ONLY mode.")
+            return
+        if not self.is_connected:
+            self.show_error_popup("Click CONNECT first.")
+            return
+        if self.require_start_pick and not self.start_pick_ready:
+            self.show_error_popup("Set approximate start location on the map first.")
+            self._prompt_pick_before_start_if_needed()
+            return
+        self.apply_runtime_settings()
+        if self.start_pick_ready and isinstance(self.start_pick_xyz, tuple) and len(self.start_pick_xyz) == 3:
+            px, py, pz = self.start_pick_xyz
+            try:
+                self.client.set_approx_pose(float(px), float(py), float(pz))
+            except Exception:
+                pass
+        self.client.start_localize_only()
+        self.worker_mode = "LOCALIZE_ONLY"
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+        self.set_status("Status: LOCALIZE_ONLY", "#ffcc00")
+
+    def on_pause(self):
+        self.client.pause_mapping()
+        self.worker_mode = "PAUSED"
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+        self.set_status("Status: PAUSED", "#ffcc00")
+
+    def on_stop(self):
+        if not self.ensure_min_stable_synced(show_error=True, set_status_msg=False):
+            return
+        self.client.stop_mapping()
+        self.worker_mode = "STOPPED"
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}")
+        if self.request_map_export_if_armed():
+            self.set_status("Status: STOPPED (SAVING MAP...)", "#ffcc00")
+        else:
+            self.set_status("Status: STOPPED", "white")
+
+    def on_record_start(self):
+        base = self.txt_name.text().strip() or "slam_recording"
+        self.client.record_start(base)
+        self.recording_enabled = True
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: ON")
+        self.set_status("Recording started.", "#ffcc00")
+
+    def on_record_stop(self):
+        base = self.txt_name.text().strip() or "slam_recording"
+        self.client.record_stop(True, base)
+        self.recording_enabled = False
+        self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: OFF")
+        self.set_status("Recording stopped.", "#ffcc00")
+
+    def on_replay_run(self):
+        fname, _ = QFileDialog.getOpenFileName(self, "Run Replay", self.maps_dir, "Replay Files (*.npz)")
+        if not fname:
+            return
+        try:
+            from SLAM_Replay import load_replay_npz, run_replay, compare_reports
+
+            frames, poses = load_replay_npz(fname)
+            report = run_replay(frames, poses=poses, export_nav=False)
+            self.last_replay_report = report
+            out = {"report": report}
+            passed = None
+            base_path = Path(self.replay_baseline_path)
+            if base_path.exists():
+                baseline = json.loads(base_path.read_text(encoding="utf-8"))
+                base_rep = baseline.get("report", baseline) if isinstance(baseline, dict) else {}
+                out["comparison"] = compare_reports(report, base_rep, tol_ratio=0.15)
+                passed = bool(out["comparison"].get("passed", False))
+            msg = json.dumps(out, indent=2)
+            QMessageBox.information(self, "Replay Report", msg)
+            if passed is None:
+                self.set_status("Replay complete (no baseline).", self.cfg["gui"]["colors"]["accent"])
+            elif passed:
+                self.set_status("Replay regression: PASS", self.cfg["gui"]["colors"]["accent"])
+            else:
+                self.set_status("Replay regression: FAIL", "#ff4444")
+        except Exception as e:
+            self.show_error_popup(f"Replay failed: {e}")
+
+    def on_replay_set_baseline(self):
+        if not isinstance(self.last_replay_report, dict):
+            self.show_error_popup("Run replay first, then set baseline.")
+            return
+        try:
+            base = {"report": self.last_replay_report}
+            Path(self.replay_baseline_path).write_text(json.dumps(base, indent=2), encoding="utf-8")
+            self.set_status(f"Baseline saved: {Path(self.replay_baseline_path).name}", "#ffcc00")
+        except Exception as e:
+            self.show_error_popup(f"Failed to write baseline: {e}")
+
+    def on_save_now(self):
+        if not self.ensure_min_stable_synced(show_error=True, set_status_msg=False):
+            return
+        base = self.txt_name.text().strip() or "map_robot"
+        self.client.export_map(base)
+        self.set_status("Saving map now...", "#ffcc00")
+
+    def on_reset(self):
+        self.save_armed = False
+        self.chk_save.blockSignals(True); self.chk_save.setChecked(False); self.chk_save.blockSignals(False)
+        self.client.reset_mapping()
+        self.worker_mode = "IDLE"
+        self.recording_enabled = False
+        self.recording_frames = 0
+        self.recording_dropped = 0
+        self.lbl_mode.setText("Mode: IDLE  Rec: OFF")
+        self.scatter.setData(pos=np.zeros((1,3),dtype=np.float32))
+        self.set_status("Status: RESET", "#ffcc00")
+        self.lbl_points.setText("Stable points: 0")
+        self.current_stable_points = 0
+
+    def on_toggle_save(self, state: int):
+        self.save_armed = bool(state)
+        self.set_status("Save armed" if self.save_armed else "Save disarmed",
+                        self.cfg["gui"]["colors"]["accent"] if self.save_armed else "white")
+
+    def on_apply_autosave(self, _checked: bool = False, set_status_msg: bool = True, quiet: bool = False):
+        sec = self._read_float(self.txt_autosave_sec, self.autosave_interval_sec)
+        sec = max(5.0, sec)
+        self.autosave_interval_sec = sec
+        self.autosave_enabled = bool(self.chk_autosave.isChecked())
+        self.txt_autosave_sec.setText(str(int(sec)))
+        base = self.txt_name.text().strip() or "autosave_map"
+        try:
+            self.client.set_autosave(self.autosave_enabled, self.autosave_interval_sec, base)
+        except Exception:
+            if not quiet:
+                self.show_error_popup("Failed to send autosave settings to worker.")
+            return
+        if set_status_msg:
+            mode = "ON" if self.autosave_enabled else "OFF"
+            self.set_status(f"Autosave {mode} ({int(self.autosave_interval_sec)}s)", "#ffcc00")
+
+    def on_export_nav(self):
+        if not self.ensure_min_stable_synced(show_error=True, set_status_msg=False):
+            return
+        self.on_apply_nav_config(set_status_msg=False, quiet=True)
+        base = self.txt_name.text().strip() or "map_robot"
+        self.client.export_nav(base)
+        self.set_status("Exporting nav map...", "#ffcc00")
+
+    def on_apply_min_stable_points(self):
+        value = self._read_min_stable_input(show_error=True)
+        if value is None:
+            return
+        self._apply_min_stable_value(int(value), set_status_msg=True)
+
+    def on_toggle_loop_closure(self, state: int):
+        self.loop_closure_enabled = bool(state)
+        try:
+            self.client.set_loop_closure(self.loop_closure_enabled)
+        except Exception:
+            pass
+        self.set_status(
+            "Loop closure SAFE ON" if self.loop_closure_enabled else "Loop closure OFF",
+            "#ffcc00",
+        )
+
+    def on_toggle_loc_machine(self, state: int):
+        self.loc_machine_enabled = bool(state)
+        try:
+            self.client.set_loc_state_machine(self.loc_machine_enabled)
+        except Exception:
+            pass
+        self.set_status(
+            "Localization state machine ON" if self.loc_machine_enabled else "Localization state machine OFF",
+            "#ffcc00",
+        )
+
+    def on_toggle_submap_mode(self, state: int):
+        self.submap_mode_enabled = bool(state)
+        try:
+            self.client.set_submap_mode(
+                self.submap_mode_enabled,
+                {"apply_profiles": ["LOCALIZE_MAP", "LIVE_NAV_MAP"]},
+            )
+        except Exception:
+            pass
+        self.set_status(
+            "Submap LOCAL+GLOBAL ON" if self.submap_mode_enabled else "Submap LOCAL+GLOBAL OFF",
+            "#ffcc00",
+        )
+
+    def on_apply_nav_config(self, _checked: bool = False, set_status_msg: bool = True, quiet: bool = False):
+        zmin = self._read_float(self.txt_nav_zmin, float(self.nav_cfg.get("z_min_m", -0.4)))
+        zmax = self._read_float(self.txt_nav_zmax, float(self.nav_cfg.get("z_max_m", 1.2)))
+        res = max(0.01, self._read_float(self.txt_nav_res, float(self.nav_cfg.get("resolution_m", 0.05))))
+        inf = max(0.0, self._read_float(self.txt_nav_inf, float(self.nav_cfg.get("inflation_radius_m", 0.2))))
+        patch = {
+            "z_min_m": zmin,
+            "z_max_m": zmax,
+            "resolution_m": res,
+            "inflation_radius_m": inf,
+            "min_points": int(self.min_stable_points),
+        }
+        self.nav_cfg.update(patch)
+        try:
+            self.client.set_nav_export_cfg(patch)
+        except Exception:
+            if not quiet:
+                self.show_error_popup("Failed to send nav tuning to worker.")
+            return
+        if set_status_msg:
+            self.set_status("Nav tuning applied.", "#ffcc00")
+
+    def on_set_nav_goal(self):
+        x = self._read_float(self.txt_goal_x, 0.0)
+        y = self._read_float(self.txt_goal_y, 0.0)
+        try:
+            self.client.set_nav_goal(x, y)
+            self.set_status(f"Nav goal set ({x:.2f}, {y:.2f})", "#ffcc00")
+        except Exception:
+            self.show_error_popup("Failed to send nav goal.")
+
+    def on_clear_nav_goal(self):
+        try:
+            self.client.clear_nav_goal()
+            self.set_status("Nav goal cleared.", "white")
+        except Exception:
+            self.show_error_popup("Failed to clear nav goal.")
+
+    def on_mission_start(self):
+        raw = self.txt_mission.text().strip()
+        if not raw:
+            self.show_error_popup("Mission waypoints are empty.")
+            return
+        waypoints = []
+        try:
+            chunks = [x.strip() for x in raw.split(";") if x.strip()]
+            for ch in chunks:
+                parts = [p.strip() for p in ch.split(",")]
+                if len(parts) < 2:
+                    continue
+                waypoints.append({"x": float(parts[0]), "y": float(parts[1])})
+        except Exception:
+            self.show_error_popup("Mission format should be: x,y; x,y; ...")
+            return
+        if not waypoints:
+            self.show_error_popup("No valid mission waypoints parsed.")
+            return
+        try:
+            self.client.mission_start(waypoints)
+            self.set_status(f"Mission started ({len(waypoints)} wp)", "#ffcc00")
+        except Exception:
+            self.show_error_popup("Failed to start mission.")
+
+    def _send_mission_cmd(self, mode: str):
+        m = str(mode).lower().strip()
+        try:
+            if m == "pause":
+                self.client.mission_pause()
+                self.set_status("Mission paused.", "#ffcc00")
+            elif m == "resume":
+                self.client.mission_resume()
+                self.set_status("Mission resumed.", "#ffcc00")
+            elif m == "stop":
+                self.client.mission_stop()
+                self.set_status("Mission stopped.", "white")
+        except Exception:
+            self.show_error_popup("Failed to send mission command.")
+
+    def on_apply_map_quality(self, _checked: bool = False, set_status_msg: bool = True, quiet: bool = False):
+        patch = {
+            "enabled": True,
+            "near_min_range_m": max(0.0, self._read_float(self.txt_near_range, 0.15)),
+            "world_z_clip_enabled": bool(self.chk_clip_z.isChecked()),
+            "world_z_min_m": self._read_float(self.txt_world_zmin, -2.0),
+            "world_z_max_m": self._read_float(self.txt_world_zmax, 3.0),
+            "outlier_filter_enabled": bool(self.chk_outlier.isChecked()),
+            "outlier_voxel_m": max(0.02, self._read_float(self.txt_outlier_voxel, 0.12)),
+            "outlier_min_points": max(1, int(self._read_float(self.txt_outlier_min, 2))),
+        }
+        self.map_quality_cfg.update(patch)
+        try:
+            self.client.set_map_quality_cfg(patch)
+        except Exception:
+            if not quiet:
+                self.show_error_popup("Failed to send map-quality settings to worker.")
+            return
+        if set_status_msg:
+            self.set_status("Map-quality filter applied.", "#ffcc00")
+
+    def refresh_density_buttons(self):
+        for mode, btn in (
+            ("LOW", self.btn_den_low),
+            ("MEDIUM", self.btn_den_med),
+            ("HIGH", self.btn_den_high),
+        ):
+            if mode == self.density_mode:
+                btn.setStyleSheet(pill_button("#0f8f4b"))
+            else:
+                btn.setStyleSheet(pill_button("#2b2b2b"))
+
+    def set_density_mode(self, mode: str):
+        self.density_mode = str(mode).upper().strip()
+        if self.density_mode not in ("LOW", "MEDIUM", "HIGH"):
+            self.density_mode = "MEDIUM"
+        self.refresh_density_buttons()
+        self.lbl_density.setText(f"Density: {self.density_mode}")
+        try:
+            self.client.set_density(self.density_mode)
+        except Exception:
+            pass
+        self.set_status(f"Density set: {self.density_mode}", "#ffcc00")
+
+    def refresh_ref_color_buttons(self):
+        for mode, btn in (
+            ("GRAY", self.btn_ref_gray),
+            ("HEIGHT", self.btn_ref_height),
+            ("ORIGINAL", self.btn_ref_orig),
+        ):
+            if mode == self.ref_color_mode:
+                btn.setStyleSheet(pill_button("#0f8f4b"))
+            else:
+                btn.setStyleSheet(pill_button("#2b2b2b"))
+
+    def _start_ref_loader(self, filename: str, show_status: bool = True):
+        if self._ref_thread is not None and self._ref_thread.isRunning():
+            self._ref_thread.quit()
+            self._ref_thread.wait(200)
+        self._ref_thread = RefLoaderThread(
+            filename,
+            display_voxel=self.ref_display_voxel,
+            color_mode=self.ref_color_mode,
+        )
+        self._ref_thread.loaded.connect(self.on_ref_loaded)
+        self._ref_thread.failed.connect(lambda e: QMessageBox.critical(self, "REF load failed", e))
+        self._ref_thread.start()
+        if show_status:
+            self.set_status(f"Ref loading ({self.ref_color_mode})...", "#ffcc00")
+
+    def set_ref_color_mode(self, mode: str):
+        m = str(mode).upper().strip()
+        if m not in ("GRAY", "HEIGHT", "ORIGINAL"):
+            m = "GRAY"
+        self.ref_color_mode = m
+        self.refresh_ref_color_buttons()
+        if self.ref_map_path:
+            self._start_ref_loader(self.ref_map_path, show_status=False)
+        self.set_status(f"Ref color: {self.ref_color_mode}", "#ffcc00")
+
+    def on_load_ref(self):
+        if not self._pick_and_load_ref_map():
+            return
+        self.set_status("Ref loaded.", "#ffcc00")
+
+    def on_ref_loaded(self, pts, cols, filename: str):
+        draw_pts = pts
+        draw_cols = cols
+        if len(draw_pts) > self.max_ref_draw:
+            step = max(2, len(draw_pts) // self.max_ref_draw)
+            draw_pts = draw_pts[::step]
+            if draw_cols is not None and len(draw_cols) == len(pts):
+                draw_cols = draw_cols[::step]
+
+        if draw_cols is not None:
+            self.ref_scatter.setData(pos=draw_pts, color=draw_cols, size=1.2)
+        else:
+            self.ref_scatter.setData(pos=draw_pts, size=1.2)
+        try:
+            pick_pts = np.asarray(pts, dtype=np.float32)
+            if pick_pts.ndim == 2 and pick_pts.shape[1] == 3 and len(pick_pts) > 0:
+                max_n = max(2000, int(self.ref_pick_max_points))
+                if len(pick_pts) > max_n:
+                    step = max(1, int(np.ceil(float(len(pick_pts)) / float(max_n))))
+                    pick_pts = pick_pts[::step][:max_n]
+                self.ref_pick_points = np.asarray(pick_pts, dtype=np.float32)
+                self.view.pick_points = self.ref_pick_points
+            else:
+                self.ref_pick_points = None
+                self.view.pick_points = None
+        except Exception:
+            self.ref_pick_points = None
+            self.view.pick_points = None
+
+        # Auto-localize only when stable map is ready; otherwise avoid error popups.
+        if self.worker_mode == "LOCALIZE_ONLY":
+            self.client.start_localize_only()
+            self.set_status("Ref shown. Localize-only running...", "#ffcc00")
+        elif self._workflow_requires_ref_map():
+            if self.require_start_pick and not self.start_pick_ready:
+                self._set_approx_pick_mode(True)
+                if self._pick_help_shown_for_ref != str(filename):
+                    self._pick_help_shown_for_ref = str(filename)
+                    QMessageBox.information(
+                        self,
+                        "Set Start Location",
+                        "Reference map loaded.\n\nClick CLICK START POINTER, then click your current robot location on the map, then press START.",
+                    )
+                self.set_status("Ref shown. Click start pointer, then click map to set start location.", "#ffcc00")
+            else:
+                self.set_status("Ref shown. Click CONNECT then START (auto localize).", "#ffcc00")
+        elif int(self.current_stable_points) >= int(self.min_stable_points):
+            self.client.localize_now()
+            self.set_status("Ref shown. Localizing...", "#ffcc00")
+        else:
+            self.set_status(
+                f"Ref shown. Need stable points {self.current_stable_points}/{self.min_stable_points} before localize.",
+                "#ffcc00",
+            )
+        self.lbl_loc.setText(
+            f"Localization: ref={Path(filename).name} ({len(pts)} pts, {self.ref_color_mode})"
+        )
+        self._refresh_pick_status()
+
+    def on_localize(self):
+        if not self.ref_map_path:
+            self.show_error_popup("Load a reference map first.")
+            return
+        if not self._workflow_requires_ref_map() and self.worker_mode != "LOCALIZE_ONLY":
+            if not self.ensure_min_stable_synced(show_error=True, set_status_msg=False):
+                return
+        self.client.localize_now()
+        self.set_status("Localization forced...", "#ffcc00")
+
+    def _refresh_pick_status(self):
+        if self.start_pick_ready and isinstance(self.start_pick_xyz, tuple) and len(self.start_pick_xyz) == 3:
+            x, y, z = self.start_pick_xyz
+            self.lbl_pick_status.setText(
+                f"Start pick: set ({self._fmt_coord(x)}, {self._fmt_coord(y)}, {self._fmt_coord(z)})"
+            )
+            self.lbl_pick_status.setStyleSheet("color:#00ff88; font-family:monospace; font-weight:800;")
+        else:
+            txt = "Start pick: optional (not required)" if not self.require_start_pick else "Start pick: pending"
+            self.lbl_pick_status.setText(txt)
+            self.lbl_pick_status.setStyleSheet("color:#ffcc00; font-family:monospace; font-weight:800;")
+
+    @staticmethod
+    def _fmt_coord(v: float, ndigits: int = 3) -> str:
+        vv = float(v)
+        if abs(vv) < (10 ** (-(ndigits + 1))):
+            vv = 0.0
+        return f"{vv:.{int(ndigits)}f}"
+
+    def _hide_start_pointer_visual(self):
+        self.start_ptr_scatter.setData(
+            pos=np.zeros((1, 3), dtype=np.float32),
+            color=np.zeros((1, 4), dtype=np.float32),
+            size=1.0,
+        )
+
+    def _set_start_pointer_visual(self, x: float, y: float, z: float):
+        pos = np.asarray([[float(x), float(y), float(z)]], dtype=np.float32)
+        col = np.asarray([[1.00, 0.82, 0.08, 0.96]], dtype=np.float32)
+        self.start_ptr_scatter.setData(pos=pos, color=col, size=19.0)
+
+    def _snap_pick_to_ref(self, x: float, y: float, z: float) -> tuple[float, float, float]:
+        pts = self.ref_pick_points
+        if pts is None:
+            return float(x), float(y), float(z)
+        arr = np.asarray(pts, dtype=np.float32)
+        if arr.ndim != 2 or arr.shape[1] != 3 or len(arr) == 0:
+            return float(x), float(y), float(z)
+        q = np.asarray([float(x), float(y)], dtype=np.float32)
+        rel = arr[:, :2] - q.reshape((1, 2))
+        d2 = np.einsum("ij,ij->i", rel, rel)
+        idx = int(np.argmin(d2))
+        if float(d2[idx]) <= float(self.ref_pick_snap_radius_m) * float(self.ref_pick_snap_radius_m):
+            p = arr[idx]
+            return float(p[0]), float(p[1]), float(p[2])
+        return float(x), float(y), float(z)
+
+    def on_toggle_require_start_pick(self, state: int):
+        self.require_start_pick = bool(state)
+        if not self.require_start_pick:
+            self._set_approx_pick_mode(False)
+        elif self._workflow_requires_ref_map() and self.ref_map_path and (not self.start_pick_ready):
+            self._set_approx_pick_mode(True)
+        self._refresh_pick_status()
+        self.set_status(
+            "Start-point pick required." if self.require_start_pick else "Start-point pick optional.",
+            "#ffcc00",
+        )
+
+    def _prompt_pick_before_start_if_needed(self):
+        if (not self._workflow_requires_ref_map()) or (not self.require_start_pick):
+            return
+        if self.start_pick_ready:
+            return
+        self._set_approx_pick_mode(True)
+        QMessageBox.information(
+            self,
+            "Set Start Location",
+            "Before START, click CLICK START POINTER, then click the reference map at robot current location.",
+        )
+
+    def _set_approx_pick_mode(self, armed: bool):
+        self.approx_pick_armed = bool(armed)
+        self.view.pick_mode = bool(armed)
+        if bool(armed):
+            self.btn_pick_approx.setStyleSheet(pill_button("#0f8f4b"))
+            self.btn_pick_approx.setText("🧭 CLICK START POINTER (ON)")
+            self.set_status("Click on map to set start pointer. Click again to move it. Press button to stop.", "#ffcc00")
+        else:
+            self.btn_pick_approx.setStyleSheet(pill_button("#2b2b2b"))
+            self.btn_pick_approx.setText("🧭 CLICK START POINTER")
+
+    def on_set_approx_pick(self):
+        if not self.ref_map_path:
+            self.show_error_popup("Load a reference map first.")
+            return
+        if self.approx_pick_armed:
+            self._set_approx_pick_mode(False)
+            self.set_status("Start pointer pick disabled.", "#ffcc00")
+            return
+        self.view.pick_plane_z = float(self.approx_guess_z)
+        self._set_approx_pick_mode(True)
+        if self.start_pick_ready and isinstance(self.start_pick_xyz, tuple) and len(self.start_pick_xyz) == 3:
+            sx, sy, _ = self.start_pick_xyz
+            self._set_start_pointer_visual(float(sx), float(sy), float(self.approx_guess_z))
+
+    def on_clear_approx(self):
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        try:
+            self.client.clear_approx_pose()
+        except Exception:
+            pass
+        self._refresh_pick_status()
+        self._hide_start_pointer_visual()
+        self.set_status("Approximate position cleared.", "#ffcc00")
+
+    def on_view_map_pick_drag(self, x: float, y: float, z: float, final: bool):
+        if not self.approx_pick_armed:
+            return
+        if not self.ref_map_path:
+            return
+        z_guess = float(z) if np.isfinite(float(z)) else float(self.approx_guess_z)
+        sx, sy, sz = self._snap_pick_to_ref(float(x), float(y), float(z_guess))
+        self.approx_guess_z = float(sz)
+        self.txt_approx_z.setText(f"{self.approx_guess_z:.2f}")
+        self._set_start_pointer_visual(float(sx), float(sy), float(sz))
+        self.start_pick_ready = True
+        self.start_pick_xyz = (float(sx), float(sy), float(sz))
+        self._refresh_pick_status()
+        if not bool(final):
+            return
+        try:
+            self.client.set_approx_pose(float(sx), float(sy), float(sz))
+            self.set_status(
+                f"Start pointer set at ({self._fmt_coord(sx)}, {self._fmt_coord(sy)}, {self._fmt_coord(sz)}). Click map again to adjust.",
+                "#ffcc00",
+            )
+        except Exception:
+            self.show_error_popup("Failed to send approximate position to worker.")
+
+    def on_clear_ref(self):
+        if self._ref_thread is not None and self._ref_thread.isRunning():
+            self._ref_thread.quit()
+            self._ref_thread.wait(200)
+        self.ref_map_path = None
+        self.ref_pick_points = None
+        self.view.pick_points = None
+        self._set_approx_pick_mode(False)
+        self.start_pick_ready = False
+        self.start_pick_xyz = None
+        self._pick_help_shown_for_ref = None
+        self.client.clear_ref()
+        try:
+            self.client.clear_approx_pose()
+        except Exception:
+            pass
+        self.ref_scatter.setData(
+            pos=np.zeros((1, 3), dtype=np.float32),
+            color=np.zeros((1, 4), dtype=np.float32),
+            size=1.0,
+        )
+        self.ref_match_scatter.setData(
+            pos=np.zeros((1, 3), dtype=np.float32),
+            color=np.zeros((1, 4), dtype=np.float32),
+            size=1.0,
+        )
+        self.lbl_loc.setText("Localization: NO_REF")
+        self._refresh_pick_status()
+        self.lbl_loc_match.setText("Match: n/a")
+        self.set_status("Ref cleared.", "white")
+
+    def update_loop(self):
+        # handle status msgs
+        try:
+            while True:
+                level, msg = self.client.status_q.get_nowait()
+                if level == "ERROR":
+                    self.set_status(f"ERR: {msg}", "#ff4444")
+                    if "CONNECT" in str(msg).upper() or "LIVOX" in str(msg).upper():
+                        self.is_connected = False
+                    self.show_error_popup(str(msg))
+                elif level == "WARN":
+                    self.set_status(f"WARN: {msg}", "#ffcc00")
+                elif level == "INFO" and isinstance(msg, str):
+                    up = msg.upper()
+                    if "CONNECTED TO LIVOX" in up:
+                        self.is_connected = True
+                        self.set_status("Status: CONNECTED", self.cfg["gui"]["colors"]["accent"])
+                    elif "MAPPING STARTED" in up:
+                        self.set_status("Status: MAPPING", self.cfg["gui"]["colors"]["accent"])
+                    elif "LOCALIZE_ONLY STARTED" in up:
+                        self.set_status("Status: LOCALIZE_ONLY", "#ffcc00")
+                    elif "MAPPING PAUSED" in up:
+                        self.set_status("Status: PAUSED", "#ffcc00")
+                    elif "MAPPING STOPPED" in up:
+                        self.set_status("Status: STOPPED", "white")
+                    elif "SAVED:" in up:
+                        saved_path = str(msg).split("SAVED:", 1)[1].strip() if "SAVED:" in str(msg) else ""
+                        if saved_path:
+                            self.set_status(f"Status: MAP SAVED ({Path(saved_path).name})", self.cfg["gui"]["colors"]["accent"])
+                            self.show_saved_popup(f"Map saved:\n{saved_path}")
+                        else:
+                            self.set_status("Status: MAP SAVED", self.cfg["gui"]["colors"]["accent"])
+                elif isinstance(msg, dict) and "NAV_EXPORTED" in msg:
+                    nav = msg.get("NAV_EXPORTED", {})
+                    pgm = str(nav.get("pgm", ""))
+                    self.set_status("Status: NAV EXPORTED", self.cfg["gui"]["colors"]["accent"])
+                    if pgm:
+                        QMessageBox.information(self, "Navigation Export", f"Saved navigation files:\n{pgm}")
+                elif isinstance(msg, dict) and "SESSION" in msg:
+                    sess = msg.get("SESSION", {})
+                    if bool(sess.get("prior_loaded", False)):
+                        ref = str(sess.get("ref", "reference"))
+                        self.set_status(f"Session prior loaded: {ref}", "#ffcc00")
+                elif isinstance(msg, dict) and "SELF_CHECK" in msg:
+                    rep = msg.get("SELF_CHECK", {})
+                    src = str(msg.get("source", "worker"))
+                    self.show_self_check_popup(rep if isinstance(rep, dict) else {}, source=src)
+                elif isinstance(msg, dict) and "LOOP" in msg:
+                    loop = msg.get("LOOP", {})
+                    if bool(loop.get("optimized", False)):
+                        self.set_status("Status: LOOP CLOSED", self.cfg["gui"]["colors"]["accent"])
+                    rej = loop.get("rejected", {})
+                    if isinstance(rej, dict) and rej:
+                        tr = float(rej.get("translation_m", 0.0))
+                        ang = float(rej.get("rotation_deg", 0.0))
+                        self.set_status(f"Loop reject safe guard ({tr:.2f}m, {ang:.1f}deg)", "#ffcc00")
+                elif isinstance(msg, dict) and "LOOP_MODE" in msg:
+                    info = msg.get("LOOP_MODE", {})
+                    enabled = bool(info.get("enabled", self.loop_closure_enabled))
+                    self.loop_closure_enabled = enabled
+                    self.chk_loop_closure.blockSignals(True)
+                    self.chk_loop_closure.setChecked(enabled)
+                    self.chk_loop_closure.blockSignals(False)
+                elif isinstance(msg, dict) and "LOC_MACHINE" in msg:
+                    info = msg.get("LOC_MACHINE", {})
+                    enabled = bool(info.get("enabled", self.loc_machine_enabled))
+                    self.loc_machine_enabled = enabled
+                    self.chk_loc_machine.blockSignals(True)
+                    self.chk_loc_machine.setChecked(enabled)
+                    self.chk_loc_machine.blockSignals(False)
+                elif isinstance(msg, dict) and "SUBMAP_MODE" in msg:
+                    info = msg.get("SUBMAP_MODE", {})
+                    enabled = bool(info.get("enabled", self.submap_mode_enabled))
+                    self.submap_mode_enabled = enabled
+                    self.chk_submap_mode.blockSignals(True)
+                    self.chk_submap_mode.setChecked(enabled)
+                    self.chk_submap_mode.blockSignals(False)
+                elif isinstance(msg, dict) and "LOC_STATE" in msg:
+                    loc_info = msg.get("LOC_STATE", {})
+                    state = str(loc_info.get("state", "TRACKING")).upper().strip()
+                    enabled = bool(loc_info.get("enabled", self.loc_machine_enabled))
+                    self.loc_machine_enabled = enabled
+                    self.chk_loc_machine.blockSignals(True)
+                    self.chk_loc_machine.setChecked(enabled)
+                    self.chk_loc_machine.blockSignals(False)
+                    self.lbl_loc_state.setText(f"Loc health: {state}")
+                    if state == "LOST":
+                        self.set_status("Status: LOST (RECOVERING)", "#ff4444")
+                    elif state == "RECOVERY":
+                        self.set_status("Status: RECOVERY (AUTO RELOCALIZE)", "#ffcc00")
+                    elif state == "DEGRADED":
+                        self.set_status("Status: DEGRADED", "#ffcc00")
+                elif isinstance(msg, dict) and "DENSITY" in msg:
+                    den = msg.get("DENSITY", {})
+                    mode = str(den.get("mode", self.density_mode)).upper().strip()
+                    stride = int(den.get("stride", 1))
+                    self.density_mode = mode if mode in ("LOW", "MEDIUM", "HIGH") else self.density_mode
+                    self.refresh_density_buttons()
+                    self.lbl_density.setText(f"Density: {self.density_mode} (stride {stride})")
+                elif isinstance(msg, dict) and "MIN_STABLE_POINTS" in msg:
+                    info = msg.get("MIN_STABLE_POINTS", {})
+                    try:
+                        value = int(info.get("value", self.min_stable_points))
+                    except Exception:
+                        value = self.min_stable_points
+                    self.min_stable_points = value
+                    self.txt_min_stable.setText(str(value))
+                    self.lbl_min_stable.setText(f"Min stable: {value}")
+                elif isinstance(msg, dict) and "AUTOSAVE" in msg:
+                    info = msg.get("AUTOSAVE", {})
+                    self.autosave_enabled = bool(info.get("enabled", self.autosave_enabled))
+                    self.autosave_interval_sec = float(info.get("interval_sec", self.autosave_interval_sec))
+                    self.chk_autosave.blockSignals(True)
+                    self.chk_autosave.setChecked(self.autosave_enabled)
+                    self.chk_autosave.blockSignals(False)
+                    self.txt_autosave_sec.setText(str(int(self.autosave_interval_sec)))
+                elif isinstance(msg, dict) and "AUTOSAVED" in msg:
+                    auto = msg.get("AUTOSAVED", {})
+                    path = str(auto.get("path", ""))
+                    pts = int(auto.get("points", 0))
+                    if path:
+                        self.set_status(f"Autosaved: {Path(path).name} ({pts} pts)", self.cfg["gui"]["colors"]["accent"])
+                elif isinstance(msg, dict) and "MODE" in msg:
+                    info = msg.get("MODE", {})
+                    self.worker_mode = str(info.get("mode", self.worker_mode)).upper().strip()
+                    self.lbl_mode.setText(
+                        f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}"
+                    )
+                elif isinstance(msg, dict) and "RECORDING" in msg:
+                    info = msg.get("RECORDING", {})
+                    self.recording_enabled = bool(info.get("enabled", self.recording_enabled))
+                    self.recording_frames = int(info.get("frames", self.recording_frames))
+                    self.recording_dropped = int(info.get("dropped", self.recording_dropped))
+                    self.lbl_mode.setText(
+                        f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'}"
+                    )
+                elif isinstance(msg, dict) and "RECORD_SAVED" in msg:
+                    rec = msg.get("RECORD_SAVED", {})
+                    path = str(rec.get("path", ""))
+                    frames = int(rec.get("frames", 0))
+                    dropped = int(rec.get("dropped", 0))
+                    if path:
+                        self.set_status(f"Recording saved ({frames} frames)", self.cfg["gui"]["colors"]["accent"])
+                        QMessageBox.information(
+                            self,
+                            "Recording Saved",
+                            f"Saved replay file:\n{path}\n\nFrames: {frames}\nDropped: {dropped}",
+                        )
+                elif isinstance(msg, dict) and "NAV_CONFIG" in msg:
+                    info = msg.get("NAV_CONFIG", {})
+                    self.txt_nav_zmin.setText(str(info.get("z_min_m", self.txt_nav_zmin.text())))
+                    self.txt_nav_zmax.setText(str(info.get("z_max_m", self.txt_nav_zmax.text())))
+                    self.txt_nav_res.setText(str(info.get("resolution_m", self.txt_nav_res.text())))
+                    self.txt_nav_inf.setText(str(info.get("inflation_radius_m", self.txt_nav_inf.text())))
+                elif isinstance(msg, dict) and "NAV_GOAL" in msg:
+                    info = msg.get("NAV_GOAL", None)
+                    if isinstance(info, dict):
+                        self.set_status(
+                            f"Nav goal set: ({float(info.get('x', 0.0)):.2f}, {float(info.get('y', 0.0)):.2f})",
+                            "#ffcc00",
+                        )
+                    else:
+                        self.set_status("Nav goal cleared.", "white")
+                elif isinstance(msg, dict) and "MISSION" in msg:
+                    info = msg.get("MISSION", {})
+                    if bool(info.get("completed", False)):
+                        self.set_status("Mission completed.", self.cfg["gui"]["colors"]["accent"])
+                elif isinstance(msg, dict) and "MAP_QUALITY" in msg:
+                    info = msg.get("MAP_QUALITY", {})
+                    self.txt_near_range.setText(str(info.get("near_min_range_m", self.txt_near_range.text())))
+                    self.chk_outlier.setChecked(bool(info.get("outlier_filter_enabled", self.chk_outlier.isChecked())))
+                    self.chk_clip_z.setChecked(bool(info.get("world_z_clip_enabled", self.chk_clip_z.isChecked())))
+                    self.txt_world_zmin.setText(str(info.get("world_z_min_m", self.txt_world_zmin.text())))
+                    self.txt_world_zmax.setText(str(info.get("world_z_max_m", self.txt_world_zmax.text())))
+                    self.txt_outlier_voxel.setText(str(info.get("outlier_voxel_m", self.txt_outlier_voxel.text())))
+                    self.txt_outlier_min.setText(str(info.get("outlier_min_points", self.txt_outlier_min.text())))
+                elif isinstance(msg, dict) and "FILTER_TUNING" in msg:
+                    info = msg.get("FILTER_TUNING", {})
+                    prof = str(info.get("profile", self.workflow_profile)).upper().strip()
+                    self._apply_workflow_ui_rules(prof, update_label=True)
+                elif isinstance(msg, dict) and "WORKFLOW_PROFILE" in msg:
+                    info = msg.get("WORKFLOW_PROFILE", {})
+                    prof = str(info.get("name", self.workflow_profile)).upper().strip()
+                    self._apply_workflow_ui_rules(prof, update_label=True)
+                elif isinstance(msg, dict) and "LOCALIZE" in msg:
+                    loc = msg["LOCALIZE"]
+                    fit = float(loc.get("fitness", 0.0))
+                    rmse = float(loc.get("rmse", 0.0))
+                    conf = float(loc.get("confidence", 0.0))
+                    self.last_loc_confidence = float(conf)
+                    ok = bool(loc.get("accepted", False))
+                    src = str(loc.get("source", "")).upper().strip()
+                    state = str(loc.get("state", self.lbl_loc_state.text().split(":")[-1].strip())).upper().strip()
+                    bi = float(loc.get("inlier_bidir", 0.0))
+                    prefix = f"{src} " if src else ""
+                    self.lbl_loc.setText(
+                        f"Localization: {prefix}fit={fit:.3f} rmse={rmse:.3f} bi={bi:.2f} conf={conf:.2f} {'OK' if ok else 'NO'}"
+                    )
+                    self.lbl_loc_state.setText(f"Loc health: {state}")
+                    if ok:
+                        self.set_status(f"Status: LOCALIZED ({src or 'RUN'})", self.cfg["gui"]["colors"]["accent"])
+                    else:
+                        self.set_status(f"Status: LOCALIZE WEAK ({src or 'RUN'})", "#ffcc00")
+                elif isinstance(msg, dict) and "APPROX_POSE" in msg:
+                    info = msg.get("APPROX_POSE", {})
+                    if bool(info.get("set", False)):
+                        ax = float(info.get("x", 0.0))
+                        ay = float(info.get("y", 0.0))
+                        az = float(info.get("z", self.approx_guess_z))
+                        self.approx_guess_z = az
+                        self.txt_approx_z.setText(f"{az:.2f}")
+                        self.start_pick_ready = True
+                        self.start_pick_xyz = (ax, ay, az)
+                        self._set_start_pointer_visual(ax, ay, az)
+                        self._refresh_pick_status()
+                        self.set_status(
+                            f"Approx pose armed at ({self._fmt_coord(ax)}, {self._fmt_coord(ay)}, {self._fmt_coord(az)})",
+                            "#ffcc00",
+                        )
+                    else:
+                        self.start_pick_ready = False
+                        self.start_pick_xyz = None
+                        self._hide_start_pointer_visual()
+                        self._refresh_pick_status()
+                        self.set_status("Approx pose cleared.", "#ffcc00")
+        except Exception:
+            pass
+
+        if self.client.proc is not None and not self.client.proc.is_alive():
+            if not self._worker_dead_notified:
+                self._worker_dead_notified = True
+                code = self.client.proc.exitcode
+                code_txt = "unknown" if code is None else str(code)
+                self.worker_mode = "DEAD"
+                self.is_connected = False
+                self.lbl_mode.setText(f"Mode: {self.worker_mode}  Rec: OFF")
+                self.set_status(f"ERR: Worker stopped (exit {code_txt})", "#ff4444")
+                if int(code or 0) == -11:
+                    # Native crash fallback: keep advanced loop closure off on next restart.
+                    was_loop_on = bool(self.loop_closure_enabled)
+                    self.loop_closure_enabled = False
+                    self.chk_loop_closure.blockSignals(True)
+                    self.chk_loop_closure.setChecked(False)
+                    self.chk_loop_closure.blockSignals(False)
+                    extra = "Advanced loop closure was disabled for safety.\n" if was_loop_on else ""
+                    self.show_error_popup(
+                        "SLAM worker process stopped (exit code -11).\n"
+                        "Native ICP/localization path crashed.\n"
+                        f"{extra}"
+                        "Click CONNECT to restart."
+                    )
+                else:
+                    self.show_error_popup(f"SLAM worker process stopped (exit code {code_txt}). Click CONNECT to restart.")
+        else:
+            self._worker_dead_notified = False
+
+        latest = None
+        try:
+            while True:
+                latest = self.client.data_q.get_nowait()
+        except Exception:
+            pass
+        if not latest:
+            return
+
+        tag, payload = latest
+        if tag != "FRAME":
+            return
+
+        pts = payload.get("stable_points", None)
+        cols = payload.get("stable_colors", None)
+        pose = payload.get("pose", None)
+        perf = payload.get("perf", {}) if isinstance(payload, dict) else {}
+        map_lock = payload.get("map_lock", {}) if isinstance(payload, dict) else {}
+        continuity = payload.get("continuity", {}) if isinstance(payload, dict) else {}
+        submap = payload.get("submap", {}) if isinstance(payload, dict) else {}
+        mode = str(payload.get("mode", self.worker_mode)).upper().strip() if isinstance(payload, dict) else self.worker_mode
+        nav = payload.get("nav", {}) if isinstance(payload, dict) else {}
+        safety = payload.get("safety", {}) if isinstance(payload, dict) else {}
+        rec = payload.get("recording", {}) if isinstance(payload, dict) else {}
+        wf = str(payload.get("workflow_profile", self.workflow_profile)).upper().strip() if isinstance(payload, dict) else self.workflow_profile
+        ref_match_points = payload.get("ref_match_points", None) if isinstance(payload, dict) else None
+        ref_match_colors = payload.get("ref_match_colors", None) if isinstance(payload, dict) else None
+        ref_match_stats = payload.get("ref_match", {}) if isinstance(payload, dict) else {}
+        self.last_loc_confidence = float(payload.get("loc_confidence", self.last_loc_confidence)) if isinstance(payload, dict) else self.last_loc_confidence
+
+        if pts is not None and len(pts) > 1:
+            draw_pts = pts
+            draw_cols = cols
+            if len(draw_pts) > self.max_draw:
+                step = max(2, len(draw_pts) // self.max_draw)
+                if self.decimate_step > 1:
+                    step = max(step, self.decimate_step)
+                draw_pts = draw_pts[::step]
+                if draw_cols is not None and len(draw_cols) == len(pts):
+                    draw_cols = draw_cols[::step]
+
+            if draw_cols is not None:
+                self.scatter.setData(pos=draw_pts, color=draw_cols)
+            else:
+                self.scatter.setData(pos=draw_pts)
+
+            self.lbl_points.setText(f"Stable points: {len(pts)}")
+            self.current_stable_points = int(len(pts))
+
+        if ref_match_points is not None:
+            try:
+                mpts = np.asarray(ref_match_points, dtype=np.float32)
+                if mpts.ndim == 2 and mpts.shape[1] == 3 and len(mpts) > 1:
+                    mcols = None
+                    if ref_match_colors is not None:
+                        mcols_arr = np.asarray(ref_match_colors, dtype=np.float32)
+                        if mcols_arr.ndim == 2 and mcols_arr.shape[1] == 4 and len(mcols_arr) == len(mpts):
+                            mcols = mcols_arr
+                    if len(mpts) > self.max_ref_draw:
+                        mstep = max(2, len(mpts) // self.max_ref_draw)
+                        mpts = mpts[::mstep]
+                        if mcols is not None:
+                            mcols = mcols[::mstep]
+                    if mcols is not None:
+                        self.ref_match_scatter.setData(pos=mpts, color=mcols, size=1.5)
+                    else:
+                        self.ref_match_scatter.setData(pos=mpts, size=1.5)
+                else:
+                    self.ref_match_scatter.setData(
+                        pos=np.zeros((1, 3), dtype=np.float32),
+                        color=np.zeros((1, 4), dtype=np.float32),
+                        size=1.0,
+                    )
+            except Exception:
+                pass
+        elif str(mode).upper().strip() != "LOCALIZE_ONLY":
+            self.ref_match_scatter.setData(
+                pos=np.zeros((1, 3), dtype=np.float32),
+                color=np.zeros((1, 4), dtype=np.float32),
+                size=1.0,
+            )
+
+        if pose is not None:
+            try:
+                x, y, z = float(pose[0, 3]), float(pose[1, 3]), float(pose[2, 3])
+                self.lbl_pose.setText(f"Pos: {x:.2f}, {y:.2f}, {z:.2f}")
+                self.axis.resetTransform()
+                self.axis.translate(x, y, z)
+            except Exception:
+                pass
+
+        if isinstance(perf, dict) and perf:
+            try:
+                in_fps = float(perf.get("input_fps", 0.0))
+                out_fps = float(perf.get("publish_fps", 0.0))
+                icp_ms = float(perf.get("icp_ms", 0.0))
+                cpu_pct = float(perf.get("cpu_percent", 0.0))
+                qlag = int(perf.get("queue_lag", 0))
+                growth = float(perf.get("stable_growth_per_sec", 0.0))
+                freeze_count = int(map_lock.get("freeze_count", 0)) if isinstance(map_lock, dict) else 0
+                c_rej_total = int(continuity.get("reject_count", 0)) if isinstance(continuity, dict) else 0
+                c_rej_streak = int(continuity.get("reject_streak", 0)) if isinstance(continuity, dict) else 0
+                self.lbl_perf_fps.setText(f"FPS in/out: {in_fps:.1f} / {out_fps:.1f}")
+                self.lbl_perf_icp.setText(f"ICP: {icp_ms:.1f} ms  CPU: {cpu_pct:.1f}%")
+                hold_txt = f"  Hold: {freeze_count}" if freeze_count > 0 else ""
+                self.lbl_perf_queue.setText(
+                    f"Queue lag: {qlag}  Growth: {growth:.1f} pts/s{hold_txt}  Cont: {c_rej_streak}/{c_rej_total}"
+                )
+            except Exception:
+                pass
+
+        if isinstance(rec, dict):
+            self.recording_enabled = bool(rec.get("enabled", self.recording_enabled))
+            self.recording_frames = int(rec.get("frames", self.recording_frames))
+            self.recording_dropped = int(rec.get("dropped", self.recording_dropped))
+
+        if mode:
+            self.worker_mode = mode
+        if wf:
+            self._apply_workflow_ui_rules(wf, update_label=True)
+        mode_suffix = ""
+        if isinstance(map_lock, dict) and bool(map_lock.get("freeze_active", False)):
+            mode_suffix = "  [ROTATE HOLD]"
+        submap_suffix = ""
+        if isinstance(submap, dict):
+            if bool(submap.get("enabled", False)) and bool(submap.get("active", False)):
+                lg = int(submap.get("local_points", 0))
+                gg = int(submap.get("global_points", 0))
+                submap_suffix = f"  Submap L/G:{lg}/{gg}"
+            elif bool(submap.get("enabled", False)):
+                submap_suffix = "  Submap ON"
+        self.lbl_mode.setText(
+            f"Mode: {self.worker_mode}  Rec: {'ON' if self.recording_enabled else 'OFF'} ({self.recording_frames}){mode_suffix}{submap_suffix}"
+        )
+
+        if isinstance(nav, dict):
+            goal = nav.get("goal", None)
+            cmd = nav.get("cmd", {}) if isinstance(nav.get("cmd", {}), dict) else {}
+            lin = float(cmd.get("linear_mps", 0.0))
+            ang = float(cmd.get("angular_rps", 0.0))
+            if goal and isinstance(goal, (list, tuple)) and len(goal) >= 2:
+                gx = float(goal[0]); gy = float(goal[1])
+                self.lbl_nav.setText(f"Nav: goal {gx:.2f},{gy:.2f}  cmd {lin:.2f}/{ang:.2f}")
+            else:
+                self.lbl_nav.setText(f"Nav: goal none  cmd {lin:.2f}/{ang:.2f}")
+
+        if isinstance(ref_match_stats, dict) and ref_match_stats:
+            matched_ratio = float(ref_match_stats.get("matched_ratio", self.last_loc_match_ratio))
+            self.last_loc_match_ratio = matched_ratio
+            matched = int(ref_match_stats.get("matched", 0))
+            near = int(ref_match_stats.get("near", 0))
+            unmatched = int(ref_match_stats.get("unmatched", 0))
+            confirmed = int(ref_match_stats.get("confirmed", 0))
+            self.lbl_loc_match.setText(
+                f"Match: {matched_ratio * 100.0:.0f}%  C:{confirmed} M:{matched} N:{near} U:{unmatched}  Conf:{self.last_loc_confidence:.2f}"
+            )
+        elif not self._workflow_requires_ref_map():
+            self.lbl_loc_match.setText("Match: n/a")
+
+        if isinstance(safety, dict) and bool(safety.get("emergency", False)):
+            reasons = safety.get("reasons", [])
+            rs = ",".join(str(x) for x in reasons[:2]) if isinstance(reasons, list) else "safety"
+            self.set_status(f"SAFETY STOP: {rs}", "#ff4444")
+
+    def closeEvent(self, event):
+        try:
+            if self.recording_enabled:
+                self.client.record_stop(True, self.txt_name.text().strip() or "slam_recording")
+            # If save is armed, request one final export before shutting down worker.
+            self.client.stop_mapping()
+            if self.request_map_export_if_armed():
+                end_t = time.monotonic() + 1.2
+                while time.monotonic() < end_t:
+                    QApplication.processEvents()
+                    time.sleep(0.05)
+            self.client.stop_process()
+        except Exception:
+            pass
+        event.accept()
+
+
+def main():
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+    app = QApplication(sys.argv)
+    app.setStyle("Fusion")
+    w = UltimateDashboard()
+    w.show()
+    sys.exit(app.exec())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Lidar/.bak-before-prod-sync/SLAM_MAP.py b/Lidar/.bak-before-prod-sync/SLAM_MAP.py
new file mode 100644
index 0000000..b3570de
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_MAP.py
@@ -0,0 +1,118 @@
+# SLAM_MAP.py
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass
+from pathlib import Path
+import numpy as np
+
+
+def load_slam_config() -> dict:
+    import json, os
+    from pathlib import Path
+
+    cfg_path = os.environ.get("SLAM_CONFIG", "").strip()
+    p = Path(cfg_path) if cfg_path else (Path(__file__).resolve().parent / "SLAM_Config.json")
+
+    if not p.exists():
+        raise FileNotFoundError(f"Missing config file: {p}")
+
+    text = p.read_text(encoding="utf-8").strip()
+    if not text:
+        raise RuntimeError("SLAM_Config.json is empty.")
+
+    return json.loads(text)
+
+
+@dataclass
+class MapConfig:
+    display_voxel: float
+    save_voxel: float
+    data_folder: str
+    save_extension: str
+
+    @staticmethod
+    def from_config_file() -> "MapConfig":
+        cfg = load_slam_config()
+        maps_dir = str(cfg["app"]["maps_dir"])
+        return MapConfig(
+            display_voxel=float(cfg["map"]["display_voxel"]),
+            save_voxel=float(cfg["map"]["save_voxel"]),
+            data_folder=maps_dir,
+            save_extension=str(cfg["map"]["save_extension"]),
+        )
+
+
+def _voxel_downsample_numpy(points: np.ndarray, voxel: float) -> np.ndarray:
+    if points is None or len(points) == 0:
+        return np.zeros((0, 3), dtype=np.float32)
+    if voxel <= 0:
+        return points.astype(np.float32, copy=False)
+
+    keys = np.floor(points / voxel).astype(np.int32)
+    uniq, inv = np.unique(keys, axis=0, return_inverse=True)
+
+    out = np.zeros((len(uniq), 3), dtype=np.float64)
+    cnt = np.zeros((len(uniq),), dtype=np.int64)
+    np.add.at(out, inv, points.astype(np.float64, copy=False))
+    np.add.at(cnt, inv, 1)
+    out /= np.maximum(cnt[:, None], 1)
+    return out.astype(np.float32)
+
+
+class StableMapLayer:
+    def __init__(self, cfg: MapConfig | None = None):
+        self.cfg = cfg if cfg is not None else MapConfig.from_config_file()
+        Path(self.cfg.data_folder).mkdir(parents=True, exist_ok=True)
+        self._points = np.zeros((0, 3), dtype=np.float32)
+
+    def reset(self):
+        self._points = np.zeros((0, 3), dtype=np.float32)
+
+    def set_points(self, pts: np.ndarray):
+        self._points = pts.astype(np.float32, copy=True) if pts is not None else np.zeros((0, 3), dtype=np.float32)
+
+    def get_points(self) -> np.ndarray:
+        return self._points
+
+    def get_display_points(self) -> np.ndarray:
+        return _voxel_downsample_numpy(self._points, float(self.cfg.display_voxel))
+
+    def get_save_points(self) -> np.ndarray:
+        return _voxel_downsample_numpy(self._points, float(self.cfg.save_voxel))
+
+    def export_map(self, base_name: str) -> str:
+        """
+        Save stable map into maps_dir (no subfolders).
+        """
+        import open3d as o3d
+
+        pts = self.get_save_points()
+        if pts is None or len(pts) < 20:
+            raise RuntimeError("Not enough stable points to save.")
+
+        base = (base_name or "").strip() or "map_robot"
+        ext = self.cfg.save_extension.strip() or ".ply"
+        if base.endswith(ext):
+            base = base[: -len(ext)]
+
+        filename = f"{base}{ext}"
+        n = 1
+        folder = Path(self.cfg.data_folder)
+        while (folder / filename).exists():
+            filename = f"{base}({n}){ext}"
+            n += 1
+
+        full = str(folder / filename)
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(pts.astype(np.float64, copy=False))
+        o3d.io.write_point_cloud(full, pcd)
+        return full
+
+    def load_map(self, filepath: str) -> np.ndarray:
+        import open3d as o3d
+        pcd = o3d.io.read_point_cloud(filepath)
+        pts = np.asarray(pcd.points).astype(np.float32)
+        self.set_points(pts)
+        return pts
diff --git a/Lidar/.bak-before-prod-sync/SLAM_NavRuntime.py b/Lidar/.bak-before-prod-sync/SLAM_NavRuntime.py
new file mode 100644
index 0000000..4bae5c7
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_NavRuntime.py
@@ -0,0 +1,514 @@
+from __future__ import annotations
+
+import heapq
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, Iterable, List, Optional, Tuple
+
+import numpy as np
+
+
+def _safe_float(v: Any, default: float) -> float:
+    try:
+        return float(v)
+    except Exception:
+        return float(default)
+
+
+def _safe_int(v: Any, default: int) -> int:
+    try:
+        return int(v)
+    except Exception:
+        return int(default)
+
+
+def _as_bool(v: Any, default: bool) -> bool:
+    if v is None:
+        return default
+    if isinstance(v, bool):
+        return v
+    if isinstance(v, (int, float)):
+        return bool(v)
+    return str(v).strip().lower() in ("1", "true", "yes", "on")
+
+
+def _inflate_binary(mask: np.ndarray, radius_cells: int) -> np.ndarray:
+    if radius_cells <= 0:
+        return mask.copy()
+    h, w = mask.shape
+    out = mask.copy()
+    rr = int(radius_cells) * int(radius_cells)
+    offsets: List[Tuple[int, int]] = []
+    for dy in range(-radius_cells, radius_cells + 1):
+        for dx in range(-radius_cells, radius_cells + 1):
+            if (dy * dy + dx * dx) <= rr:
+                offsets.append((dy, dx))
+    for dy, dx in offsets:
+        ys = max(0, -dy)
+        ye = min(h, h - dy)
+        xs = max(0, -dx)
+        xe = min(w, w - dx)
+        yd = max(0, dy)
+        xd = max(0, dx)
+        out[yd : yd + (ye - ys), xd : xd + (xe - xs)] |= mask[ys:ye, xs:xe]
+    return out
+
+
+@dataclass
+class LiveCostmapConfig:
+    enabled: bool = True
+    resolution_m: float = 0.10
+    z_min_m: float = -0.40
+    z_max_m: float = 1.20
+    padding_m: float = 0.80
+    inflation_radius_m: float = 0.25
+    dynamic_decay_sec: float = 1.5
+    dynamic_min_hits: int = 2
+    blocked_cost: int = 220
+    max_width_cells: int = 900
+    max_height_cells: int = 900
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "LiveCostmapConfig":
+        src = d or {}
+        return LiveCostmapConfig(
+            enabled=_as_bool(src.get("enabled", True), True),
+            resolution_m=max(0.02, _safe_float(src.get("resolution_m", 0.10), 0.10)),
+            z_min_m=_safe_float(src.get("z_min_m", -0.40), -0.40),
+            z_max_m=_safe_float(src.get("z_max_m", 1.20), 1.20),
+            padding_m=max(0.0, _safe_float(src.get("padding_m", 0.80), 0.80)),
+            inflation_radius_m=max(0.0, _safe_float(src.get("inflation_radius_m", 0.25), 0.25)),
+            dynamic_decay_sec=max(0.2, _safe_float(src.get("dynamic_decay_sec", 1.5), 1.5)),
+            dynamic_min_hits=max(1, _safe_int(src.get("dynamic_min_hits", 2), 2)),
+            blocked_cost=int(np.clip(_safe_int(src.get("blocked_cost", 220), 220), 100, 255)),
+            max_width_cells=max(100, _safe_int(src.get("max_width_cells", 900), 900)),
+            max_height_cells=max(100, _safe_int(src.get("max_height_cells", 900), 900)),
+        )
+
+    def patched(self, patch: Dict[str, Any] | None) -> "LiveCostmapConfig":
+        src = patch or {}
+        return LiveCostmapConfig.from_dict(
+            {
+                "enabled": src.get("enabled", self.enabled),
+                "resolution_m": src.get("resolution_m", self.resolution_m),
+                "z_min_m": src.get("z_min_m", self.z_min_m),
+                "z_max_m": src.get("z_max_m", self.z_max_m),
+                "padding_m": src.get("padding_m", self.padding_m),
+                "inflation_radius_m": src.get("inflation_radius_m", self.inflation_radius_m),
+                "dynamic_decay_sec": src.get("dynamic_decay_sec", self.dynamic_decay_sec),
+                "dynamic_min_hits": src.get("dynamic_min_hits", self.dynamic_min_hits),
+                "blocked_cost": src.get("blocked_cost", self.blocked_cost),
+                "max_width_cells": src.get("max_width_cells", self.max_width_cells),
+                "max_height_cells": src.get("max_height_cells", self.max_height_cells),
+            }
+        )
+
+
+class LiveCostmapRuntime:
+    """
+    Maintains real-time static + dynamic + inflated obstacle layers.
+    Static layer comes from SLAM stable points; dynamic layer comes from live points.
+    """
+
+    def __init__(self, cfg: LiveCostmapConfig):
+        self.cfg = cfg
+        self._dynamic_cells: Dict[Tuple[int, int], Tuple[int, float]] = {}
+        self._grid: Optional[Dict[str, Any]] = None
+
+    def reset(self) -> None:
+        self._dynamic_cells.clear()
+        self._grid = None
+
+    def _clip_nav_band(self, points: Optional[np.ndarray]) -> np.ndarray:
+        if points is None:
+            return np.zeros((0, 3), dtype=np.float32)
+        pts = np.asarray(points, dtype=np.float32)
+        if pts.ndim != 2 or pts.shape[1] < 3 or len(pts) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        zmin = float(self.cfg.z_min_m)
+        zmax = float(self.cfg.z_max_m)
+        m = (pts[:, 2] >= zmin) & (pts[:, 2] <= zmax)
+        return pts[m, :3]
+
+    @staticmethod
+    def _grid_indices(xy: np.ndarray, min_xy: np.ndarray, res: float, w: int, h: int) -> Tuple[np.ndarray, np.ndarray]:
+        gx = np.floor((xy[:, 0] - min_xy[0]) / res).astype(np.int32)
+        gy = np.floor((xy[:, 1] - min_xy[1]) / res).astype(np.int32)
+        gx = np.clip(gx, 0, w - 1)
+        gy = np.clip(gy, 0, h - 1)
+        return gx, gy
+
+    def world_to_grid(self, x: float, y: float) -> Optional[Tuple[int, int]]:
+        if self._grid is None:
+            return None
+        origin = np.asarray(self._grid["origin_xy"], dtype=np.float64)
+        res = float(self._grid["resolution_m"])
+        shape = tuple(self._grid["shape_hw"])
+        h, w = int(shape[0]), int(shape[1])
+        gx = int(math.floor((float(x) - origin[0]) / res))
+        gy = int(math.floor((float(y) - origin[1]) / res))
+        if gx < 0 or gy < 0 or gx >= w or gy >= h:
+            return None
+        return gx, gy
+
+    def grid_to_world(self, gx: int, gy: int) -> Optional[Tuple[float, float]]:
+        if self._grid is None:
+            return None
+        origin = np.asarray(self._grid["origin_xy"], dtype=np.float64)
+        res = float(self._grid["resolution_m"])
+        x = origin[0] + (float(gx) + 0.5) * res
+        y = origin[1] + (float(gy) + 0.5) * res
+        return float(x), float(y)
+
+    def cost_at_world(self, x: float, y: float) -> int:
+        if self._grid is None:
+            return 0
+        idx = self.world_to_grid(x, y)
+        if idx is None:
+            return 255
+        gx, gy = idx
+        cost = np.asarray(self._grid["costmap"], dtype=np.uint8)
+        return int(cost[gy, gx])
+
+    def occupied_near(self, x: float, y: float, radius_m: float, cost_thresh: Optional[int] = None) -> bool:
+        if self._grid is None:
+            return False
+        thresh = int(self.cfg.blocked_cost if cost_thresh is None else cost_thresh)
+        idx = self.world_to_grid(x, y)
+        if idx is None:
+            return True
+        gx, gy = idx
+        cost = np.asarray(self._grid["costmap"], dtype=np.uint8)
+        h, w = cost.shape
+        rc = int(math.ceil(max(0.0, float(radius_m)) / float(self._grid["resolution_m"])))
+        ys = max(0, gy - rc)
+        ye = min(h, gy + rc + 1)
+        xs = max(0, gx - rc)
+        xe = min(w, gx + rc + 1)
+        return bool(np.any(cost[ys:ye, xs:xe] >= thresh))
+
+    def _compute_bounds(self, static_pts: np.ndarray, live_pts: np.ndarray) -> Optional[Tuple[np.ndarray, np.ndarray]]:
+        has_static = len(static_pts) > 0
+        has_live = len(live_pts) > 0
+        if not has_static and not has_live:
+            return None
+        if has_static and has_live:
+            all_xy = np.vstack((static_pts[:, :2], live_pts[:, :2]))
+        elif has_static:
+            all_xy = static_pts[:, :2]
+        else:
+            all_xy = live_pts[:, :2]
+        pad = float(self.cfg.padding_m)
+        mn = all_xy.min(axis=0) - pad
+        mx = all_xy.max(axis=0) + pad
+        return mn.astype(np.float64), mx.astype(np.float64)
+
+    def _clamp_grid_size(self, mn: np.ndarray, mx: np.ndarray, res: float) -> Tuple[np.ndarray, np.ndarray, float, int, int]:
+        span = np.maximum(mx - mn, res)
+        w = int(np.ceil(span[0] / res)) + 1
+        h = int(np.ceil(span[1] / res)) + 1
+        res_out = float(res)
+        if w > int(self.cfg.max_width_cells) or h > int(self.cfg.max_height_cells):
+            sx = float(w) / float(self.cfg.max_width_cells)
+            sy = float(h) / float(self.cfg.max_height_cells)
+            scale = max(sx, sy)
+            res_out = res_out * scale
+            span = np.maximum(mx - mn, res_out)
+            w = int(np.ceil(span[0] / res_out)) + 1
+            h = int(np.ceil(span[1] / res_out)) + 1
+        return mn, mx, float(res_out), int(w), int(h)
+
+    def update(
+        self,
+        stable_points_world: Optional[np.ndarray],
+        live_points_world: Optional[np.ndarray],
+        now: float,
+    ) -> Optional[Dict[str, Any]]:
+        if not self.cfg.enabled:
+            return None
+
+        static_pts = self._clip_nav_band(stable_points_world)
+        live_pts = self._clip_nav_band(live_points_world)
+
+        bounds = self._compute_bounds(static_pts, live_pts)
+        if bounds is None:
+            # decay dynamic memory even when there is no input
+            self._dynamic_cells = {
+                k: v
+                for k, v in self._dynamic_cells.items()
+                if (float(now) - float(v[1])) <= float(self.cfg.dynamic_decay_sec)
+            }
+            return None
+
+        mn, mx = bounds
+        mn, mx, res, w, h = self._clamp_grid_size(mn, mx, float(self.cfg.resolution_m))
+
+        static_occ = np.zeros((h, w), dtype=bool)
+        if len(static_pts) > 0:
+            gx, gy = self._grid_indices(static_pts[:, :2], mn, res, w, h)
+            static_occ[gy, gx] = True
+
+        # age out dynamic memory first
+        max_age = float(self.cfg.dynamic_decay_sec)
+        new_dyn: Dict[Tuple[int, int], Tuple[int, float]] = {}
+        for (cx, cy), (hits, ts) in self._dynamic_cells.items():
+            if (float(now) - float(ts)) <= max_age and 0 <= cx < w and 0 <= cy < h:
+                new_dyn[(cx, cy)] = (int(hits), float(ts))
+        self._dynamic_cells = new_dyn
+
+        if len(live_pts) > 0:
+            gx_l, gy_l = self._grid_indices(live_pts[:, :2], mn, res, w, h)
+            for gx, gy in zip(gx_l.tolist(), gy_l.tolist()):
+                if static_occ[gy, gx]:
+                    continue
+                k = (int(gx), int(gy))
+                old_hits, _ = self._dynamic_cells.get(k, (0, float(now)))
+                self._dynamic_cells[k] = (int(old_hits) + 1, float(now))
+
+        dynamic_occ = np.zeros((h, w), dtype=bool)
+        min_hits = int(self.cfg.dynamic_min_hits)
+        for (gx, gy), (hits, ts) in self._dynamic_cells.items():
+            if hits >= min_hits and (float(now) - float(ts)) <= max_age:
+                if 0 <= gx < w and 0 <= gy < h:
+                    dynamic_occ[gy, gx] = True
+
+        occ = static_occ | dynamic_occ
+        inf_cells = int(np.ceil(float(self.cfg.inflation_radius_m) / res))
+        inflated = _inflate_binary(occ, inf_cells)
+
+        cost = np.zeros((h, w), dtype=np.uint8)
+        cost[inflated] = 180
+        cost[dynamic_occ] = 220
+        cost[static_occ] = 255
+
+        out = {
+            "costmap": cost,
+            "static_mask": static_occ,
+            "dynamic_mask": dynamic_occ,
+            "inflated_mask": inflated,
+            "origin_xy": mn.astype(np.float32),
+            "resolution_m": float(res),
+            "shape_hw": [int(h), int(w)],
+            "static_cells": int(np.count_nonzero(static_occ)),
+            "dynamic_cells": int(np.count_nonzero(dynamic_occ)),
+            "inflated_cells": int(np.count_nonzero(inflated)),
+        }
+        self._grid = out
+        return {
+            "shape": [int(h), int(w)],
+            "resolution_m": float(res),
+            "origin_xy": [float(mn[0]), float(mn[1])],
+            "static_cells": int(out["static_cells"]),
+            "dynamic_cells": int(out["dynamic_cells"]),
+            "inflated_cells": int(out["inflated_cells"]),
+        }
+
+    @property
+    def grid(self) -> Optional[Dict[str, Any]]:
+        return self._grid
+
+
+class GlobalAStarPlanner:
+    def __init__(self, blocked_cost: int = 220):
+        self.blocked_cost = int(np.clip(int(blocked_cost), 100, 255))
+
+    @staticmethod
+    def _heur(a: Tuple[int, int], b: Tuple[int, int]) -> float:
+        return float(math.hypot(float(a[0] - b[0]), float(a[1] - b[1])))
+
+    @staticmethod
+    def _neighbors(x: int, y: int, w: int, h: int) -> Iterable[Tuple[int, int, float]]:
+        for dy in (-1, 0, 1):
+            for dx in (-1, 0, 1):
+                if dx == 0 and dy == 0:
+                    continue
+                nx = x + dx
+                ny = y + dy
+                if 0 <= nx < w and 0 <= ny < h:
+                    step = 1.41421356 if (dx != 0 and dy != 0) else 1.0
+                    yield nx, ny, step
+
+    def plan(
+        self,
+        costmap: np.ndarray,
+        origin_xy: np.ndarray,
+        resolution_m: float,
+        start_xy: Tuple[float, float],
+        goal_xy: Tuple[float, float],
+        max_expansions: int = 120000,
+    ) -> List[Tuple[float, float]]:
+        cost = np.asarray(costmap, dtype=np.uint8)
+        if cost.ndim != 2:
+            return []
+        h, w = cost.shape
+        origin = np.asarray(origin_xy, dtype=np.float64)
+        res = float(resolution_m)
+
+        def to_grid(x: float, y: float) -> Optional[Tuple[int, int]]:
+            gx = int(math.floor((float(x) - origin[0]) / res))
+            gy = int(math.floor((float(y) - origin[1]) / res))
+            if gx < 0 or gy < 0 or gx >= w or gy >= h:
+                return None
+            return gx, gy
+
+        def to_world(gx: int, gy: int) -> Tuple[float, float]:
+            return (
+                float(origin[0] + (float(gx) + 0.5) * res),
+                float(origin[1] + (float(gy) + 0.5) * res),
+            )
+
+        s = to_grid(float(start_xy[0]), float(start_xy[1]))
+        g = to_grid(float(goal_xy[0]), float(goal_xy[1]))
+        if s is None or g is None:
+            return []
+        if int(cost[s[1], s[0]]) >= self.blocked_cost or int(cost[g[1], g[0]]) >= self.blocked_cost:
+            return []
+
+        open_heap: List[Tuple[float, float, Tuple[int, int]]] = []
+        heapq.heappush(open_heap, (self._heur(s, g), 0.0, s))
+        came_from: Dict[Tuple[int, int], Tuple[int, int]] = {}
+        g_score: Dict[Tuple[int, int], float] = {s: 0.0}
+        closed: Dict[Tuple[int, int], bool] = {}
+
+        expansions = 0
+        while open_heap:
+            _, cur_g, cur = heapq.heappop(open_heap)
+            if closed.get(cur, False):
+                continue
+            closed[cur] = True
+            if cur == g:
+                break
+            expansions += 1
+            if expansions >= int(max_expansions):
+                import sys
+                print(
+                    f"[A*] expansion limit {max_expansions} reached; no path found "
+                    f"from {start_xy} to {goal_xy}",
+                    file=sys.stderr,
+                )
+                return []
+
+            cx, cy = cur
+            for nx, ny, step in self._neighbors(cx, cy, w, h):
+                if int(cost[ny, nx]) >= self.blocked_cost:
+                    continue
+                ng = float(cur_g + step + (float(cost[ny, nx]) / 255.0) * 2.0)
+                node = (nx, ny)
+                if ng < float(g_score.get(node, 1e18)):
+                    g_score[node] = ng
+                    came_from[node] = cur
+                    f = ng + self._heur(node, g)
+                    heapq.heappush(open_heap, (f, ng, node))
+
+        if g not in came_from and g != s:
+            return []
+
+        path_cells: List[Tuple[int, int]] = [g]
+        cur = g
+        visited_back: set = {g}
+        while cur != s:
+            nxt = came_from.get(cur)
+            if nxt is None or nxt in visited_back:
+                return []
+            visited_back.add(nxt)
+            cur = nxt
+            path_cells.append(cur)
+        path_cells.reverse()
+        return [to_world(px, py) for (px, py) in path_cells]
+
+
+@dataclass
+class LocalPlannerConfig:
+    lookahead_m: float = 0.8
+    max_linear_mps: float = 0.6
+    max_angular_rps: float = 1.3
+    goal_tolerance_m: float = 0.30
+    collision_probe_m: float = 0.6
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "LocalPlannerConfig":
+        src = d or {}
+        return LocalPlannerConfig(
+            lookahead_m=max(0.1, _safe_float(src.get("lookahead_m", 0.8), 0.8)),
+            max_linear_mps=max(0.05, _safe_float(src.get("max_linear_mps", 0.6), 0.6)),
+            max_angular_rps=max(0.1, _safe_float(src.get("max_angular_rps", 1.3), 1.3)),
+            goal_tolerance_m=max(0.05, _safe_float(src.get("goal_tolerance_m", 0.30), 0.30)),
+            collision_probe_m=max(0.1, _safe_float(src.get("collision_probe_m", 0.6), 0.6)),
+        )
+
+
+class LocalReactivePlanner:
+    def __init__(self, cfg: LocalPlannerConfig):
+        self.cfg = cfg
+
+    @staticmethod
+    def _yaw_from_pose(pose: np.ndarray) -> float:
+        # ZYX yaw from rotation matrix
+        return float(math.atan2(float(pose[1, 0]), float(pose[0, 0])))
+
+    @staticmethod
+    def _wrap_pi(a: float) -> float:
+        while a > math.pi:
+            a -= 2.0 * math.pi
+        while a < -math.pi:
+            a += 2.0 * math.pi
+        return a
+
+    def compute_command(
+        self,
+        pose_world: np.ndarray,
+        path_world: List[Tuple[float, float]],
+        runtime: LiveCostmapRuntime,
+    ) -> Dict[str, Any]:
+        cmd = {
+            "linear_mps": 0.0,
+            "angular_rps": 0.0,
+            "goal_reached": False,
+            "blocked": False,
+        }
+        if pose_world is None or np.asarray(pose_world).shape != (4, 4):
+            return cmd
+        if not path_world:
+            return cmd
+
+        p = np.asarray(pose_world, dtype=np.float64)
+        x = float(p[0, 3])
+        y = float(p[1, 3])
+        yaw = self._yaw_from_pose(p)
+
+        goal_x, goal_y = float(path_world[-1][0]), float(path_world[-1][1])
+        dist_goal = float(math.hypot(goal_x - x, goal_y - y))
+        if dist_goal <= float(self.cfg.goal_tolerance_m):
+            cmd["goal_reached"] = True
+            return cmd
+
+        target = path_world[-1]
+        for wx, wy in path_world:
+            d = float(math.hypot(float(wx) - x, float(wy) - y))
+            if d >= float(self.cfg.lookahead_m):
+                target = (float(wx), float(wy))
+                break
+
+        dx = float(target[0] - x)
+        dy = float(target[1] - y)
+        target_yaw = float(math.atan2(dy, dx))
+        yaw_err = self._wrap_pi(target_yaw - yaw)
+
+        ang = float(np.clip(1.5 * yaw_err, -float(self.cfg.max_angular_rps), float(self.cfg.max_angular_rps)))
+        lin_scale = max(0.0, 1.0 - min(1.0, abs(yaw_err) / math.pi))
+        lin = float(self.cfg.max_linear_mps) * lin_scale
+
+        # Probe short horizon for immediate collision.
+        n_probe = 6
+        step = float(self.cfg.collision_probe_m) / float(n_probe)
+        for i in range(1, n_probe + 1):
+            px = x + float(i) * step * math.cos(yaw)
+            py = y + float(i) * step * math.sin(yaw)
+            if runtime.cost_at_world(px, py) >= int(runtime.cfg.blocked_cost):
+                lin = 0.0
+                cmd["blocked"] = True
+                break
+
+        cmd["linear_mps"] = float(lin)
+        cmd["angular_rps"] = float(ang)
+        return cmd
diff --git a/Lidar/.bak-before-prod-sync/SLAM_Navigation.py b/Lidar/.bak-before-prod-sync/SLAM_Navigation.py
new file mode 100644
index 0000000..887e3b5
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_Navigation.py
@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, Tuple
+
+import numpy as np
+
+
+def _safe_float(v: Any, default: float) -> float:
+    try:
+        return float(v)
+    except Exception:
+        return float(default)
+
+
+def _safe_int(v: Any, default: int) -> int:
+    try:
+        return int(v)
+    except Exception:
+        return int(default)
+
+
+def _as_bool(v: Any, default: bool) -> bool:
+    if v is None:
+        return default
+    if isinstance(v, bool):
+        return v
+    if isinstance(v, (int, float)):
+        return bool(v)
+    return str(v).strip().lower() in ("1", "true", "yes", "on")
+
+
+@dataclass
+class NavigationExportConfig:
+    enabled: bool = True
+    min_points: int = 400
+    resolution_m: float = 0.05
+    z_min_m: float = 0.05   # 5 cm above floor — avoids ground returns, captures low obstacles
+    z_max_m: float = 1.40   # 1.4 m — covers G1 Edu body (1.25 m) with margin
+    inflation_radius_m: float = 0.20
+    padding_m: float = 0.40
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "NavigationExportConfig":
+        src = d or {}
+        return NavigationExportConfig(
+            enabled=_as_bool(src.get("enabled", True), True),
+            min_points=max(50, _safe_int(src.get("min_points", 400), 400)),
+            resolution_m=max(0.01, _safe_float(src.get("resolution_m", 0.05), 0.05)),
+            z_min_m=_safe_float(src.get("z_min_m", -0.40), -0.40),
+            z_max_m=_safe_float(src.get("z_max_m", 1.20), 1.20),
+            inflation_radius_m=max(0.0, _safe_float(src.get("inflation_radius_m", 0.20), 0.20)),
+            padding_m=max(0.0, _safe_float(src.get("padding_m", 0.40), 0.40)),
+        )
+
+    def patched(self, patch: Dict[str, Any] | None) -> "NavigationExportConfig":
+        src = patch or {}
+        cur = self
+        return NavigationExportConfig(
+            enabled=_as_bool(src.get("enabled", cur.enabled), cur.enabled),
+            min_points=max(50, _safe_int(src.get("min_points", cur.min_points), cur.min_points)),
+            resolution_m=max(0.01, _safe_float(src.get("resolution_m", cur.resolution_m), cur.resolution_m)),
+            z_min_m=_safe_float(src.get("z_min_m", cur.z_min_m), cur.z_min_m),
+            z_max_m=_safe_float(src.get("z_max_m", cur.z_max_m), cur.z_max_m),
+            inflation_radius_m=max(
+                0.0,
+                _safe_float(src.get("inflation_radius_m", cur.inflation_radius_m), cur.inflation_radius_m),
+            ),
+            padding_m=max(0.0, _safe_float(src.get("padding_m", cur.padding_m), cur.padding_m)),
+        )
+
+
+def _inflate_binary(mask: np.ndarray, radius_cells: int) -> np.ndarray:
+    if radius_cells <= 0:
+        return mask.copy()
+    h, w = mask.shape
+    out = mask.copy()
+    offsets = []
+    rr = radius_cells * radius_cells
+    for dy in range(-radius_cells, radius_cells + 1):
+        for dx in range(-radius_cells, radius_cells + 1):
+            if (dy * dy + dx * dx) <= rr:
+                offsets.append((dy, dx))
+    for dy, dx in offsets:
+        ys = max(0, -dy)
+        ye = min(h, h - dy)
+        xs = max(0, -dx)
+        xe = min(w, w - dx)
+        yd = max(0, dy)
+        xd = max(0, dx)
+        out[yd : yd + (ye - ys), xd : xd + (xe - xs)] |= mask[ys:ye, xs:xe]
+    return out
+
+
+def _sanitize_basename(name: str) -> str:
+    base = (name or "").strip() or "map"
+    for bad in ("/", "\\", ":", "*", "?", "\"", "<", ">", "|"):
+        base = base.replace(bad, "_")
+    return base
+
+
+class NavigationExporter:
+    def __init__(self, cfg: NavigationExportConfig, data_folder: str):
+        self.cfg = cfg
+        self.data_folder = Path(data_folder)
+        self.data_folder.mkdir(parents=True, exist_ok=True)
+
+    def update_config(self, cfg: NavigationExportConfig) -> None:
+        self.cfg = cfg
+
+    def _filter_nav_points(self, points: np.ndarray) -> np.ndarray:
+        pts = np.asarray(points, dtype=np.float32)
+        if pts.ndim != 2 or pts.shape[1] < 3:
+            raise RuntimeError("Invalid point array for navigation export.")
+        zmin, zmax = float(self.cfg.z_min_m), float(self.cfg.z_max_m)
+        m = (pts[:, 2] >= zmin) & (pts[:, 2] <= zmax)
+        return pts[m]
+
+    def count_nav_points(self, points: np.ndarray) -> int:
+        try:
+            return int(len(self._filter_nav_points(points)))
+        except Exception:
+            return 0
+
+    def _build_grid(self, points: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        pts = self._filter_nav_points(points)
+        if len(pts) < int(self.cfg.min_points):
+            raise RuntimeError(f"Not enough points for nav export ({len(pts)}).")
+
+        res = float(self.cfg.resolution_m)
+        pad = float(self.cfg.padding_m)
+
+        min_xy = pts[:, :2].min(axis=0) - pad
+        max_xy = pts[:, :2].max(axis=0) + pad
+        span = np.maximum(max_xy - min_xy, res)
+        w = int(np.ceil(span[0] / res)) + 1
+        h = int(np.ceil(span[1] / res)) + 1
+
+        occ = np.zeros((h, w), dtype=bool)
+        gx = np.clip(((pts[:, 0] - min_xy[0]) / res).astype(np.int32), 0, w - 1)
+        gy = np.clip(((pts[:, 1] - min_xy[1]) / res).astype(np.int32), 0, h - 1)
+        occ[gy, gx] = True
+
+        rad_cells = int(np.ceil(float(self.cfg.inflation_radius_m) / res))
+        inf = _inflate_binary(occ, rad_cells)
+
+        return occ, inf, min_xy
+
+    def export(self, base_name: str, points: np.ndarray) -> Dict[str, Any]:
+        if not self.cfg.enabled:
+            raise RuntimeError("Navigation export is disabled in config.")
+
+        occ, inf, min_xy = self._build_grid(points)
+        base = _sanitize_basename(base_name)
+        folder = self.data_folder
+
+        pgm_name = f"{base}_nav.pgm"
+        yaml_name = f"{base}_nav.yaml"
+        cost_name = f"{base}_costmap.npy"
+        n = 1
+        while (folder / pgm_name).exists() or (folder / yaml_name).exists() or (folder / cost_name).exists():
+            pgm_name = f"{base}_nav({n}).pgm"
+            yaml_name = f"{base}_nav({n}).yaml"
+            cost_name = f"{base}_costmap({n}).npy"
+            n += 1
+
+        # ROS map image: 0=occupied, 254=free
+        img = np.where(inf, 0, 254).astype(np.uint8)
+        img = np.flipud(img)  # image origin top-left, map origin bottom-left
+
+        pgm_path = folder / pgm_name
+        with open(pgm_path, "wb") as f:
+            h, w = img.shape
+            f.write(f"P5\n{w} {h}\n255\n".encode("ascii"))
+            f.write(img.tobytes())
+
+        yaml_path = folder / yaml_name
+        # Write proper YAML — NOT json.dumps (quoted keys break ROS2 Nav2 map_server)
+        ox, oy = float(min_xy[0]), float(min_xy[1])
+        yaml_lines = [
+            f"image: {pgm_name}",
+            f"resolution: {float(self.cfg.resolution_m):.6f}",
+            f"origin: [{ox:.6f}, {oy:.6f}, 0.000000]",
+            "negate: 0",
+            "occupied_thresh: 0.65",
+            "free_thresh: 0.196",
+            "mode: trinary",  # required by Nav2 map_server
+        ]
+        yaml_path.write_text("\n".join(yaml_lines) + "\n", encoding="utf-8")
+
+        cost = np.zeros_like(img, dtype=np.uint8)
+        occ_img = np.flipud(occ)
+        inf_img = np.flipud(inf)
+        cost[inf_img] = 180
+        cost[occ_img] = 255
+        cost_path = folder / cost_name
+        np.save(cost_path, cost)
+
+        return {
+            "pgm": str(pgm_path),
+            "yaml": str(yaml_path),
+            "costmap": str(cost_path),
+            "shape": [int(img.shape[0]), int(img.shape[1])],
+            "occupied_cells": int(np.count_nonzero(occ)),
+            "inflated_cells": int(np.count_nonzero(inf)),
+        }
diff --git a/Lidar/.bak-before-prod-sync/SLAM_Safety.py b/Lidar/.bak-before-prod-sync/SLAM_Safety.py
new file mode 100644
index 0000000..210f983
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_Safety.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import numpy as np
+
+
+
+def _safe_float(v: Any, default: float) -> float:
+    try:
+        return float(v)
+    except Exception:
+        return float(default)
+
+
+@dataclass
+class SafetyConfig:
+    enabled: bool = True
+    stop_radius_m: float = 0.50  # G1 Edu shoulder width ~0.45 m; 0.50 m gives safe margin
+    stale_localization_sec: float = 1.5
+    emergency_hold_sec: float = 0.8
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "SafetyConfig":
+        src = d or {}
+        return SafetyConfig(
+            enabled=bool(src.get("enabled", True)),
+            stop_radius_m=max(0.05, _safe_float(src.get("stop_radius_m", 0.50), 0.50)),
+            stale_localization_sec=max(0.2, _safe_float(src.get("stale_localization_sec", 1.5), 1.5)),
+            emergency_hold_sec=max(0.0, _safe_float(src.get("emergency_hold_sec", 0.8), 0.8)),
+        )
+
+
+class SafetySupervisor:
+    def __init__(self, cfg: SafetyConfig):
+        self.cfg = cfg
+        self._last_localize_ok_t = 0.0
+        self._last_emergency_t = -1e9
+
+    def mark_localization(self, ok: bool, now: float) -> None:
+        if ok:
+            self._last_localize_ok_t = float(now)
+
+    def evaluate(
+        self,
+        now: float,
+        pose_world: Optional[np.ndarray],
+        loc_state: str,
+        nav_runtime: Any,
+        nav_cmd: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        if not self.cfg.enabled:
+            return {
+                "enabled": False,
+                "emergency": False,
+                "reasons": [],
+                "cmd": dict(nav_cmd),
+            }
+
+        reasons = []
+        motion_requested = (
+            abs(float(nav_cmd.get("linear_mps", 0.0))) > 1e-3
+            or abs(float(nav_cmd.get("angular_rps", 0.0))) > 1e-3
+        )
+        lost = str(loc_state).upper().strip() == "LOST"
+        if lost and motion_requested:
+            reasons.append("localization_lost")
+
+        stale = False
+        if self._last_localize_ok_t > 0.0:
+            stale = (float(now) - float(self._last_localize_ok_t)) > float(self.cfg.stale_localization_sec)
+        if stale and motion_requested:
+            reasons.append("localization_stale")
+
+        collision = False
+        if pose_world is None or np.asarray(pose_world).shape != (4, 4):
+            if motion_requested:
+                collision = True
+                reasons.append("pose_invalid")
+        else:
+            x = float(pose_world[0, 3])
+            y = float(pose_world[1, 3])
+            try:
+                collision = bool(nav_runtime.occupied_near(x, y, float(self.cfg.stop_radius_m))) if motion_requested else False
+            except Exception:
+                collision = False
+            if collision:
+                reasons.append("collision_zone")
+
+        emergency = bool((lost and motion_requested) or (stale and motion_requested) or collision)
+        if emergency:
+            self._last_emergency_t = float(now)
+
+        hold_active = (float(now) - float(self._last_emergency_t)) <= float(self.cfg.emergency_hold_sec)
+        cmd_out = dict(nav_cmd)
+        if emergency or hold_active:
+            cmd_out["linear_mps"] = 0.0
+            cmd_out["angular_rps"] = 0.0
+            cmd_out["blocked"] = True
+
+        return {
+            "enabled": True,
+            "emergency": bool(emergency),
+            "hold": bool(hold_active),
+            "reasons": reasons,
+            "cmd": cmd_out,
+        }
diff --git a/Lidar/.bak-before-prod-sync/SLAM_Submap.py b/Lidar/.bak-before-prod-sync/SLAM_Submap.py
new file mode 100644
index 0000000..bf15e0f
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_Submap.py
@@ -0,0 +1,413 @@
+from __future__ import annotations
+
+import os
+import pickle
+import sys
+import threading
+from collections import deque
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Deque, Dict, Iterable, Optional, Tuple
+
+import numpy as np
+
+
+def _as_bool(v: Any, default: bool) -> bool:
+    if v is None:
+        return default
+    if isinstance(v, bool):
+        return v
+    if isinstance(v, (int, float)):
+        return bool(v)
+    return str(v).strip().lower() in ("1", "true", "yes", "on")
+
+
+def _safe_int(v: Any, default: int) -> int:
+    try:
+        return int(v)
+    except Exception:
+        return int(default)
+
+
+def _safe_float(v: Any, default: float) -> float:
+    try:
+        return float(v)
+    except Exception:
+        return float(default)
+
+
+def _safe_profiles(v: Any, default: Tuple[str, ...]) -> Tuple[str, ...]:
+    if isinstance(v, (list, tuple)):
+        out = []
+        for item in v:
+            s = str(item).upper().strip()
+            if s:
+                out.append(s)
+        if out:
+            return tuple(out)
+    return tuple(default)
+
+
+def _voxel_downsample(points: np.ndarray, voxel_m: float) -> np.ndarray:
+    pts = np.asarray(points, dtype=np.float32)
+    if pts.ndim != 2 or pts.shape[1] != 3 or len(pts) == 0:
+        return np.zeros((0, 3), dtype=np.float32)
+    v = float(voxel_m)
+    if v <= 0.0:
+        return pts
+    keys = np.floor(pts / v).astype(np.int32)
+    _, idx = np.unique(keys, axis=0, return_index=True)
+    return pts[np.sort(idx)]
+
+
+def _pose_delta(pose_a: np.ndarray, pose_b: np.ndarray) -> Tuple[float, float]:
+    dp = pose_a[:3, 3] - pose_b[:3, 3]
+    trans = float(np.linalg.norm(dp))
+    r = pose_a[:3, :3] @ pose_b[:3, :3].T
+    ang = float(np.degrees(np.arccos(np.clip((np.trace(r) - 1.0) * 0.5, -1.0, 1.0))))
+    return trans, ang
+
+
+@dataclass
+class SubmapConfig:
+    enabled: bool = True
+    local_window_frames: int = 18
+    local_voxel_m: float = 0.08
+    global_voxel_m: float = 0.14
+    merge_period_sec: float = 0.8
+    merge_min_translation_m: float = 0.25
+    merge_min_rotation_deg: float = 6.0
+    max_global_points: int = 350000
+    display_max_points: int = 250000
+    apply_profiles: Tuple[str, ...] = ("LOCALIZE_MAP", "LIVE_NAV_MAP")
+
+    @staticmethod
+    def from_dict(d: Dict[str, Any] | None) -> "SubmapConfig":
+        src = d or {}
+        return SubmapConfig(
+            enabled=_as_bool(src.get("enabled", True), True),
+            local_window_frames=max(4, _safe_int(src.get("local_window_frames", 18), 18)),
+            local_voxel_m=max(0.02, _safe_float(src.get("local_voxel_m", 0.08), 0.08)),
+            global_voxel_m=max(0.02, _safe_float(src.get("global_voxel_m", 0.14), 0.14)),
+            merge_period_sec=max(0.1, _safe_float(src.get("merge_period_sec", 0.8), 0.8)),
+            merge_min_translation_m=max(
+                0.01, _safe_float(src.get("merge_min_translation_m", 0.25), 0.25)
+            ),
+            merge_min_rotation_deg=max(
+                0.1, _safe_float(src.get("merge_min_rotation_deg", 6.0), 6.0)
+            ),
+            max_global_points=max(20000, _safe_int(src.get("max_global_points", 350000), 350000)),
+            display_max_points=max(10000, _safe_int(src.get("display_max_points", 250000), 250000)),
+            apply_profiles=_safe_profiles(
+                src.get("apply_profiles", ("LOCALIZE_MAP", "LIVE_NAV_MAP")),
+                ("LOCALIZE_MAP", "LIVE_NAV_MAP"),
+            ),
+        )
+
+
+class SubmapCheckpointer:
+    """
+    Periodically saves LocalGlobalSubmapMapper state to disk so the accumulated
+    map survives restarts and crashes.
+
+    Usage
+    -----
+    ckpt = SubmapCheckpointer(save_dir, interval_s=60.0)
+    # in your main loop:
+    ckpt.maybe_save(mapper)
+    # on startup:
+    ckpt.load_into(mapper)   # returns True if data was restored
+    # on shutdown:
+    ckpt.stop()
+    """
+
+    _FILENAME = "submap_checkpoint.pkl"
+    _PROTO = 5  # pickle protocol (requires Python ≥ 3.8)
+
+    def __init__(self, save_dir: str, interval_s: float = 60.0) -> None:
+        self._dir = Path(save_dir)
+        self._dir.mkdir(parents=True, exist_ok=True)
+        self._interval = max(5.0, float(interval_s))
+        self._dst = self._dir / self._FILENAME
+        self._tmp = self._dir / (self._FILENAME + ".tmp")
+        self._last_save_t: float = 0.0
+        self._lock = threading.Lock()
+
+    def _payload(self, mapper: "LocalGlobalSubmapMapper") -> dict:
+        """Snapshot all persistent fields from the mapper."""
+        return {
+            "global_pts": np.array(mapper._global_pts, dtype=np.float32, copy=True),
+            "frames": [np.array(f, dtype=np.float32, copy=True) for f in mapper._frames],
+            "last_merge_t": float(mapper._last_merge_t),
+            "last_merge_pose": (
+                np.array(mapper._last_merge_pose, dtype=np.float64, copy=True)
+                if mapper._last_merge_pose is not None
+                else None
+            ),
+            "merge_count": int(mapper._merge_count),
+            "insert_count": int(mapper._insert_count),
+        }
+
+    def maybe_save(self, mapper: "LocalGlobalSubmapMapper") -> bool:
+        """Save if the configured interval has elapsed. Thread-safe. Returns True on save."""
+        import time
+        now = time.time()
+        with self._lock:
+            if (now - self._last_save_t) < self._interval:
+                return False
+            self._last_save_t = now
+
+        return self._write(self._payload(mapper))
+
+    def save(self, mapper: "LocalGlobalSubmapMapper") -> bool:
+        """Force an immediate save. Returns True on success."""
+        return self._write(self._payload(mapper))
+
+    def _write(self, payload: dict) -> bool:
+        """Atomic write: pickle to .tmp then rename to avoid corrupt checkpoints."""
+        try:
+            with open(self._tmp, "wb") as f:
+                pickle.dump(payload, f, protocol=self._PROTO)
+            os.replace(self._tmp, self._dst)  # atomic on POSIX
+            return True
+        except Exception as exc:
+            print(f"[SubmapCheckpointer] save failed: {exc}", file=sys.stderr)
+            return False
+
+    def load_into(self, mapper: "LocalGlobalSubmapMapper") -> bool:
+        """
+        Restore checkpoint into mapper. Returns True if data was loaded.
+        Must be called before the mapper receives any frames.
+        """
+        if not self._dst.exists():
+            return False
+        try:
+            with open(self._dst, "rb") as f:
+                payload = pickle.load(f)
+            global_pts = np.asarray(payload["global_pts"], dtype=np.float32)
+            frames_raw = payload.get("frames", [])
+            mapper._global_pts = global_pts
+            mapper._frames = deque(
+                [np.asarray(fr, dtype=np.float32) for fr in frames_raw],
+                maxlen=int(mapper._frames.maxlen or mapper.cfg.local_window_frames),
+            )
+            mapper._last_merge_t = float(payload.get("last_merge_t", 0.0))
+            lmp = payload.get("last_merge_pose")
+            mapper._last_merge_pose = (
+                np.asarray(lmp, dtype=np.float64) if lmp is not None else None
+            )
+            mapper._merge_count = int(payload.get("merge_count", 0))
+            mapper._insert_count = int(payload.get("insert_count", 0))
+            mapper._rebuild_local()
+            mapper._global_pts = mapper._trim(mapper._global_pts, int(mapper.cfg.max_global_points))
+            return True
+        except Exception as exc:
+            print(f"[SubmapCheckpointer] load failed (starting fresh): {exc}", file=sys.stderr)
+            return False
+
+    def delete(self) -> None:
+        """Remove the checkpoint file (e.g., after a deliberate RESET)."""
+        try:
+            if self._dst.exists():
+                self._dst.unlink()
+            if self._tmp.exists():
+                self._tmp.unlink()
+        except Exception:
+            pass
+
+    def stop(self) -> None:
+        """No-op — kept for API symmetry; saves are driven by maybe_save() calls."""
+        pass
+
+
+class LocalGlobalSubmapMapper:
+    """
+    Maintains a short-horizon local submap and periodically merges it into a
+    long-horizon global submap.
+    """
+
+    def __init__(self, cfg: SubmapConfig):
+        self.cfg = cfg
+        self._frames: Deque[np.ndarray] = deque(maxlen=int(cfg.local_window_frames))
+        self._local_pts = np.zeros((0, 3), dtype=np.float32)
+        self._global_pts = np.zeros((0, 3), dtype=np.float32)
+        self._last_merge_t = 0.0
+        self._last_merge_pose: Optional[np.ndarray] = None
+        self._merge_count = 0
+        self._insert_count = 0
+
+    def set_config(self, cfg: SubmapConfig, keep_points: bool = True) -> None:
+        old_frames = list(self._frames) if keep_points else []
+        old_global = np.array(self._global_pts, dtype=np.float32, copy=True) if keep_points else np.zeros((0, 3), dtype=np.float32)
+        old_last_pose = np.array(self._last_merge_pose, dtype=np.float64, copy=True) if (keep_points and self._last_merge_pose is not None) else None
+        old_last_merge_t = float(self._last_merge_t) if keep_points else 0.0
+        old_merge_count = int(self._merge_count) if keep_points else 0
+        old_insert_count = int(self._insert_count) if keep_points else 0
+
+        self.cfg = cfg
+        self._frames = deque(maxlen=int(cfg.local_window_frames))
+        if keep_points and old_frames:
+            for fr in old_frames[-int(cfg.local_window_frames):]:
+                self._frames.append(np.asarray(fr, dtype=np.float32))
+        self._local_pts = np.zeros((0, 3), dtype=np.float32)
+        self._global_pts = np.asarray(old_global, dtype=np.float32) if keep_points else np.zeros((0, 3), dtype=np.float32)
+        self._last_merge_pose = old_last_pose
+        self._last_merge_t = old_last_merge_t
+        self._merge_count = old_merge_count
+        self._insert_count = old_insert_count
+        self._rebuild_local()
+        self._global_pts = self._trim(self._global_pts, int(self.cfg.max_global_points))
+
+    def reset(self) -> None:
+        self._frames.clear()
+        self._local_pts = np.zeros((0, 3), dtype=np.float32)
+        self._global_pts = np.zeros((0, 3), dtype=np.float32)
+        self._last_merge_t = 0.0
+        self._last_merge_pose = None
+        self._merge_count = 0
+        self._insert_count = 0
+
+    def apply_correction(self, transform: np.ndarray) -> bool:
+        """
+        Apply a rigid transform to all currently held submap data.
+        Useful when localization updates the global frame estimate and we
+        want old submap points to remain consistent with new incoming points.
+        """
+        if not self.has_points:
+            return False
+        tf = np.asarray(transform, dtype=np.float64)
+        if tf.shape != (4, 4):
+            return False
+        R = np.asarray(tf[:3, :3], dtype=np.float64)
+        t = np.asarray(tf[:3, 3], dtype=np.float64)
+        try:
+            if len(self._global_pts) > 0:
+                self._global_pts = np.asarray((self._global_pts @ R.T) + t, dtype=np.float32)
+            if len(self._frames) > 0:
+                frames_tx = deque(maxlen=int(self._frames.maxlen or len(self._frames)))
+                for fr in self._frames:
+                    fr_np = np.asarray(fr, dtype=np.float32)
+                    if len(fr_np) == 0:
+                        frames_tx.append(fr_np)
+                    else:
+                        frames_tx.append(np.asarray((fr_np @ R.T) + t, dtype=np.float32))
+                self._frames = frames_tx
+            if self._last_merge_pose is not None and np.asarray(self._last_merge_pose).shape == (4, 4):
+                self._last_merge_pose = tf @ np.asarray(self._last_merge_pose, dtype=np.float64)
+            self._rebuild_local()
+            self._global_pts = self._trim(self._global_pts, int(self.cfg.max_global_points))
+            return True
+        except Exception:
+            return False
+
+    @property
+    def has_points(self) -> bool:
+        return bool(len(self._local_pts) > 0 or len(self._global_pts) > 0)
+
+    def _trim(self, pts: np.ndarray, max_n: int) -> np.ndarray:
+        n = int(len(pts))
+        if n <= int(max_n):
+            return pts
+        stride = max(2, int(np.ceil(float(n) / float(max_n))))
+        out = pts[::stride]
+        if len(out) > int(max_n):
+            out = out[: int(max_n)]
+        return np.asarray(out, dtype=np.float32)
+
+    def _rebuild_local(self) -> None:
+        if len(self._frames) == 0:
+            self._local_pts = np.zeros((0, 3), dtype=np.float32)
+            return
+        cat = np.concatenate(list(self._frames), axis=0)
+        ds = _voxel_downsample(cat, float(self.cfg.local_voxel_m))
+        self._local_pts = self._trim(ds, max(5000, int(self.cfg.display_max_points)))
+
+    def _should_merge(self, now: float, pose_world: Optional[np.ndarray]) -> bool:
+        if len(self._local_pts) == 0:
+            return False
+        if len(self._global_pts) == 0:
+            return True
+        if (float(now) - float(self._last_merge_t)) >= float(self.cfg.merge_period_sec):
+            return True
+        if pose_world is None or self._last_merge_pose is None:
+            return False
+        try:
+            pose = np.asarray(pose_world, dtype=np.float64)
+            if pose.shape != (4, 4):
+                return False
+            trans, ang = _pose_delta(pose, self._last_merge_pose)
+            return bool(
+                trans >= float(self.cfg.merge_min_translation_m)
+                or ang >= float(self.cfg.merge_min_rotation_deg)
+            )
+        except Exception:
+            return False
+
+    def integrate(
+        self,
+        points_world: np.ndarray,
+        now: float,
+        pose_world: Optional[np.ndarray] = None,
+    ) -> Dict[str, Any]:
+        pts = np.asarray(points_world, dtype=np.float32)
+        if pts.ndim != 2 or pts.shape[1] != 3 or len(pts) == 0:
+            return self.status(active=False, reason="empty")
+
+        ds = _voxel_downsample(pts, float(self.cfg.local_voxel_m))
+        if len(ds) == 0:
+            return self.status(active=False, reason="empty_downsample")
+
+        self._frames.append(ds)
+        self._insert_count += 1
+        self._rebuild_local()
+
+        merged = False
+        if self._should_merge(float(now), pose_world):
+            if len(self._global_pts) == 0:
+                combo = np.array(self._local_pts, dtype=np.float32, copy=True)
+            else:
+                combo = np.concatenate([self._global_pts, self._local_pts], axis=0)
+            self._global_pts = _voxel_downsample(combo, float(self.cfg.global_voxel_m))
+            self._global_pts = self._trim(self._global_pts, int(self.cfg.max_global_points))
+            self._last_merge_t = float(now)
+            if pose_world is not None:
+                pose = np.asarray(pose_world, dtype=np.float64)
+                if pose.shape == (4, 4):
+                    self._last_merge_pose = np.array(pose, dtype=np.float64, copy=True)
+            merged = True
+            self._merge_count += 1
+
+        out = self.status(active=True, reason="ok")
+        out["merged"] = bool(merged)
+        return out
+
+    def get_display_points(self) -> np.ndarray:
+        if len(self._global_pts) == 0 and len(self._local_pts) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        if len(self._global_pts) == 0:
+            return np.asarray(self._local_pts, dtype=np.float32)
+        if len(self._local_pts) == 0:
+            return self._trim(np.asarray(self._global_pts, dtype=np.float32), int(self.cfg.display_max_points))
+        combo = np.concatenate([self._global_pts, self._local_pts], axis=0)
+        disp = _voxel_downsample(combo, float(self.cfg.local_voxel_m))
+        return self._trim(disp, int(self.cfg.display_max_points))
+
+    def status(
+        self,
+        active: bool,
+        reason: str = "ok",
+        profile: str = "",
+        profile_allowed: bool = True,
+    ) -> Dict[str, Any]:
+        return {
+            "enabled": bool(self.cfg.enabled),
+            "active": bool(active),
+            "reason": str(reason),
+            "profile": str(profile).upper().strip(),
+            "profile_allowed": bool(profile_allowed),
+            "local_points": int(len(self._local_pts)),
+            "global_points": int(len(self._global_pts)),
+            "window_frames": int(len(self._frames)),
+            "merge_count": int(self._merge_count),
+            "insert_count": int(self._insert_count),
+        }
diff --git a/Lidar/.bak-before-prod-sync/SLAM_Transforms.py b/Lidar/.bak-before-prod-sync/SLAM_Transforms.py
new file mode 100644
index 0000000..4d3cdab
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_Transforms.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from typing import Optional, Tuple
+
+import numpy as np
+
+
+def to_world_points(points_sensor: np.ndarray, pose: Optional[np.ndarray]) -> np.ndarray:
+    """
+    Convert sensor-frame points to world frame using pose (world_T_sensor).
+    Falls back to input points when pose is unavailable/invalid.
+    """
+    if pose is None:
+        return np.asarray(points_sensor, dtype=np.float32)
+    try:
+        pts = np.asarray(points_sensor, dtype=np.float32)
+        pose_np = np.asarray(pose, dtype=np.float32)
+        if pts.ndim != 2 or pts.shape[1] != 3 or pose_np.shape != (4, 4):
+            return pts
+        rot = pose_np[:3, :3]
+        trans = pose_np[:3, 3]
+        return (pts @ rot.T) + trans
+    except Exception:
+        return np.asarray(points_sensor, dtype=np.float32)
+
+
+def apply_transform_points(points: Optional[np.ndarray], transform: np.ndarray) -> Optional[np.ndarray]:
+    if points is None:
+        return None
+    pts = np.asarray(points, dtype=np.float32)
+    if len(pts) == 0:
+        return pts
+    try:
+        tf = np.asarray(transform, dtype=np.float32)
+        if tf.shape != (4, 4):
+            return pts
+        rot = tf[:3, :3]
+        trans = tf[:3, 3]
+        return (pts @ rot.T) + trans
+    except Exception:
+        return pts
+
+
+def tf_delta(prev_tf: np.ndarray, new_tf: np.ndarray) -> Tuple[float, float]:
+    try:
+        a = np.asarray(prev_tf, dtype=np.float64)
+        b = np.asarray(new_tf, dtype=np.float64)
+        if a.shape != (4, 4) or b.shape != (4, 4):
+            return 0.0, 0.0
+        dp = b[:3, 3] - a[:3, 3]
+        trans = float(np.linalg.norm(dp))
+        r = b[:3, :3] @ a[:3, :3].T
+        ang = float(np.degrees(np.arccos(np.clip((np.trace(r) - 1.0) * 0.5, -1.0, 1.0))))
+        return trans, ang
+    except Exception:
+        return 0.0, 0.0
+
+
+def blend_rigid_tf(prev_tf: np.ndarray, new_tf: np.ndarray, alpha_t: float, alpha_r: float) -> np.ndarray:
+    try:
+        a = np.asarray(prev_tf, dtype=np.float64)
+        b = np.asarray(new_tf, dtype=np.float64)
+        if a.shape != (4, 4) or b.shape != (4, 4):
+            return np.asarray(new_tf, dtype=np.float64)
+        at = float(np.clip(alpha_t, 0.0, 1.0))
+        ar = float(np.clip(alpha_r, 0.0, 1.0))
+        out = np.eye(4, dtype=np.float64)
+        out[:3, 3] = ((1.0 - at) * a[:3, 3]) + (at * b[:3, 3])
+        rm = ((1.0 - ar) * a[:3, :3]) + (ar * b[:3, :3])
+        u, _, vt = np.linalg.svd(rm, full_matrices=False)
+        r = u @ vt
+        if np.linalg.det(r) < 0:
+            u[:, -1] *= -1.0
+            r = u @ vt
+        out[:3, :3] = r
+        return out
+    except Exception:
+        return np.asarray(new_tf, dtype=np.float64)
+
+
+def yaw_deg_from_tf(tf: np.ndarray) -> float:
+    try:
+        m = np.asarray(tf, dtype=np.float64)
+        if m.shape != (4, 4):
+            return 0.0
+        yaw = np.degrees(np.arctan2(float(m[1, 0]), float(m[0, 0])))
+        return float(yaw)
+    except Exception:
+        return 0.0
+
+
+def tf_from_xyzyaw(x: float, y: float, z: float, yaw_deg: float) -> np.ndarray:
+    yaw = np.deg2rad(float(yaw_deg))
+    cy = float(np.cos(yaw))
+    sy = float(np.sin(yaw))
+    tf = np.eye(4, dtype=np.float64)
+    tf[:3, :3] = np.array(
+        [[cy, -sy, 0.0], [sy, cy, 0.0], [0.0, 0.0, 1.0]],
+        dtype=np.float64,
+    )
+    tf[:3, 3] = np.array([float(x), float(y), float(z)], dtype=np.float64)
+    return tf
diff --git a/Lidar/.bak-before-prod-sync/SLAM_engine.py b/Lidar/.bak-before-prod-sync/SLAM_engine.py
new file mode 100644
index 0000000..5742853
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_engine.py
@@ -0,0 +1,318 @@
+# SLAM_engine.py
+from __future__ import annotations
+
+import json
+import os
+import time
+import multiprocessing as mp
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Optional
+
+from SLAM_Validation import run_startup_self_check
+
+# ------------------------- config loader (jsonl) -------------------------
+def load_slam_config() -> dict:
+    """
+    Loads config from:
+      1) env SLAM_CONFIG (full path)
+      2) ./SLAM_Config.json (same folder as this script)
+    """
+    import json
+    import os
+    from pathlib import Path
+
+    cfg_path = os.environ.get("SLAM_CONFIG", "").strip()
+    if cfg_path:
+        p = Path(cfg_path)
+    else:
+        p = Path(__file__).resolve().parent / "SLAM_Config.json"
+
+    if not p.exists():
+        raise FileNotFoundError(f"Missing config file: {p}")
+
+    text = p.read_text(encoding="utf-8").strip()
+    if not text:
+        raise RuntimeError("SLAM_Config.json is empty.")
+
+    return json.loads(text)
+
+
+def _config_base_dir() -> Path:
+    cfg_path = os.environ.get("SLAM_CONFIG", "").strip()
+    if cfg_path:
+        return Path(cfg_path).expanduser().resolve().parent
+    return Path(__file__).resolve().parent
+
+
+def _resolve_from_config_dir(path_value: str | os.PathLike[str]) -> str:
+    p = Path(path_value).expanduser()
+    if p.is_absolute():
+        return str(p)
+    return str((_config_base_dir() / p).resolve())
+
+
+
+# ------------------------- dataclasses -------------------------
+@dataclass
+class EngineConfig:
+    config_file: str
+    host_ip: str
+    max_range: float
+    slam_voxel_size: float
+    pre_downsample_stride: int
+    keyframe_enabled: bool
+    keyframe_min_translation_m: float
+    keyframe_min_rotation_deg: float
+    tag_filter: bool = True
+
+
+@dataclass
+class FilterConfig:
+    voxel_size: float
+    hits_threshold: int
+    window_sec: float
+    strict_sec: float
+    use_strict: bool
+    decay_seconds: float
+    max_voxels: int
+
+
+@dataclass
+class MapConfig:
+    data_folder: str
+    display_voxel: float
+    save_voxel: float
+    min_points_to_save: int
+    save_extension: str
+
+
+@dataclass
+class LocalizationConfig:
+    enabled: bool
+    period_sec: float
+    min_new_points: int
+    min_points_for_localize: int
+    voxel_localize: float
+    max_corr_mult: float
+    icp_max_iter: int
+    accept_fitness: float
+    accept_rmse: float
+    ref_display_voxel: float
+
+
+@dataclass
+class RuntimeConfig:
+    publish_hz: float
+    frame_keep_latest: bool
+    frame_queue_maxsize: int
+    status_queue_maxsize: int
+    cmd_queue_maxsize: int
+
+
+# ------------------------- helpers -------------------------
+def _drain_keep_latest(q: mp.Queue) -> None:
+    try:
+        while True:
+            q.get_nowait()
+    except Exception:
+        return
+
+
+def _safe_put(q: mp.Queue, item: Any, keep_latest: bool = False) -> None:
+    try:
+        if keep_latest:
+            _drain_keep_latest(q)
+        q.put_nowait(item)
+    except Exception:
+        pass
+
+
+def build_configs_from_json(cfg: dict) -> tuple[EngineConfig, FilterConfig, MapConfig, LocalizationConfig, RuntimeConfig]:
+    maps_dir = _resolve_from_config_dir(str(cfg["app"]["maps_dir"]))
+
+    eng = EngineConfig(
+        config_file=_resolve_from_config_dir(str(cfg["livox"]["config_file"])),
+        host_ip=cfg["network"]["default_host_ip"],
+        max_range=float(cfg["slam"]["max_range"]),
+        slam_voxel_size=float(cfg["slam"]["slam_voxel_size"]),
+        pre_downsample_stride=int(cfg["slam"]["pre_downsample_stride"]),
+        keyframe_enabled=bool(cfg["slam"]["keyframe"]["enabled"]),
+        keyframe_min_translation_m=float(cfg["slam"]["keyframe"]["min_translation_m"]),
+        keyframe_min_rotation_deg=float(cfg["slam"]["keyframe"]["min_rotation_deg"]),
+        tag_filter=bool(cfg.get("livox", {}).get("tag_filter", True)),
+    )
+
+    filt = FilterConfig(
+        voxel_size=float(cfg["filter"]["voxel_size"]),
+        hits_threshold=int(cfg["filter"]["hits_threshold"]),
+        window_sec=float(cfg["filter"]["window_sec"]),
+        strict_sec=float(cfg["filter"]["strict_sec"]),
+        use_strict=bool(cfg["filter"]["use_strict"]),
+        decay_seconds=float(cfg["filter"]["persistence"]["decay_seconds"]),
+        max_voxels=int(cfg["filter"]["persistence"]["max_voxels"]),
+    )
+
+    mp_cfg = MapConfig(
+        data_folder=str(maps_dir),
+        display_voxel=float(cfg["map"]["display_voxel"]),
+        save_voxel=float(cfg["map"]["save_voxel"]),
+        min_points_to_save=int(cfg["map"]["min_points_to_save"]),
+        save_extension=str(cfg["map"]["save_extension"]),
+    )
+
+    loc = LocalizationConfig(
+        enabled=bool(cfg["localization"]["enabled"]),
+        period_sec=float(cfg["localization"]["period_sec"]),
+        min_new_points=int(cfg["localization"]["min_new_points"]),
+        min_points_for_localize=int(cfg["localization"]["min_points_for_localize"]),
+        voxel_localize=float(cfg["localization"]["voxel_localize"]),
+        max_corr_mult=float(cfg["localization"]["max_corr_mult"]),
+        icp_max_iter=int(cfg["localization"]["icp_max_iter"]),
+        accept_fitness=float(cfg["localization"]["accept_fitness"]),
+        accept_rmse=float(cfg["localization"]["accept_rmse"]),
+        ref_display_voxel=float(cfg["localization"]["ref_display_voxel"]),
+    )
+
+    run = RuntimeConfig(
+        publish_hz=float(cfg["runtime"]["publish_hz"]),
+        frame_keep_latest=bool(cfg["runtime"]["queue"]["frame_keep_latest"]),
+        frame_queue_maxsize=int(cfg["runtime"]["queue"]["frame_queue_maxsize"]),
+        status_queue_maxsize=int(cfg["runtime"]["queue"]["status_queue_maxsize"]),
+        cmd_queue_maxsize=int(cfg["runtime"]["queue"]["cmd_queue_maxsize"]),
+    )
+    return eng, filt, mp_cfg, loc, run
+
+
+# ------------------------- client -------------------------
+class SlamEngineClient:
+    def __init__(self):
+        self.cfg_json = load_slam_config()
+        self.self_check = run_startup_self_check(self.cfg_json, _config_base_dir())
+        self.eng_cfg, self.filt_cfg, self.map_cfg, self.loc_cfg, self.run_cfg = build_configs_from_json(self.cfg_json)
+
+        ctx = mp.get_context("spawn")
+        self.data_q: mp.Queue = ctx.Queue(maxsize=self.run_cfg.frame_queue_maxsize)
+        self.status_q: mp.Queue = ctx.Queue(maxsize=self.run_cfg.status_queue_maxsize)
+        self.cmd_q: mp.Queue = ctx.Queue(maxsize=self.run_cfg.cmd_queue_maxsize)
+        self.proc: Optional[mp.Process] = None
+
+    def start_process(self):
+        if self.proc is not None and self.proc.is_alive():
+            return
+
+        ctx = mp.get_context("spawn")
+        self.data_q = ctx.Queue(maxsize=self.run_cfg.frame_queue_maxsize)
+        self.status_q = ctx.Queue(maxsize=self.run_cfg.status_queue_maxsize)
+        self.cmd_q = ctx.Queue(maxsize=self.run_cfg.cmd_queue_maxsize)
+
+        from SLAM_worker import slam_worker
+
+        self.proc = ctx.Process(
+            target=slam_worker,
+            args=(self.data_q, self.status_q, self.cmd_q, self.eng_cfg, self.filt_cfg, self.map_cfg, self.loc_cfg, self.run_cfg),
+            daemon=True,
+        )
+        self.proc.start()
+
+    def stop_process(self):
+        try:
+            self.send("SHUTDOWN")
+        except Exception:
+            pass
+        if self.proc is not None:
+            self.proc.join(timeout=1.0)
+            if self.proc.is_alive():
+                self.proc.terminate()
+                self.proc.join(timeout=1.0)
+
+    def send(self, cmd: Any):
+        if self.proc is None or not self.proc.is_alive():
+            self.start_process()
+        self.cmd_q.put(cmd)
+
+    def connect(self): self.send("CONNECT")
+    def start_mapping(self): self.send("START")
+    def pause_mapping(self): self.send("PAUSE")
+    def stop_mapping(self): self.send("STOP")
+    def reset_mapping(self): self.send("RESET")
+    def export_map(self, filename_base: str): self.send(("EXPORT", filename_base))
+    def export_nav(self, filename_base: str): self.send(("EXPORT_NAV", filename_base))
+    def load_ref_map(self, path: str): self.send(("LOAD_REF", path))
+    def load_for_extend(self, path: str): self.send(("LOAD_FOR_EXTEND", path))
+    def localize_now(self): self.send("LOCALIZE")
+    def clear_ref(self): self.send("CLEAR_REF")
+    def set_density(self, mode: str): self.send(("SET_DENSITY", str(mode)))
+    def set_min_stable_points(self, value: int): self.send(("SET_MIN_STABLE_POINTS", int(value)))
+    def set_loop_closure(self, enabled: bool): self.send(("SET_LOOP_CLOSURE", bool(enabled)))
+    def set_loc_state_machine(self, enabled: bool): self.send(("SET_LOC_STATE_MACHINE", bool(enabled)))
+    def set_submap_mode(self, enabled: bool, cfg_patch: Optional[dict] = None):
+        payload = {"enabled": bool(enabled)}
+        if isinstance(cfg_patch, dict) and cfg_patch:
+            payload.update(dict(cfg_patch))
+        self.send(("SET_SUBMAP_MODE", payload))
+    def set_approx_pose(self, x: float, y: float, z: float = 0.0, yaw_deg: Optional[float] = None):
+        payload = {"x": float(x), "y": float(y), "z": float(z)}
+        if yaw_deg is not None:
+            payload["yaw_deg"] = float(yaw_deg)
+        self.send(("SET_APPROX_POSE", payload))
+    def clear_approx_pose(self):
+        self.send("CLEAR_APPROX_POSE")
+    def set_autosave(self, enabled: bool, interval_sec: float, base_name: str):
+        self.send(
+            (
+                "SET_AUTOSAVE",
+                {
+                    "enabled": bool(enabled),
+                    "interval_sec": float(interval_sec),
+                    "base_name": str(base_name),
+                },
+            )
+        )
+    def set_nav_export_cfg(self, cfg_patch: dict):
+        self.send(("SET_NAV_CONFIG", dict(cfg_patch)))
+    def set_nav_runtime_cfg(self, cfg_patch: dict):
+        self.send(("SET_NAV_RUNTIME", dict(cfg_patch)))
+    def set_map_quality_cfg(self, cfg_patch: dict):
+        self.send(("SET_MAP_QUALITY", dict(cfg_patch)))
+    def set_filter_tuning(self, cfg_patch: dict):
+        self.send(("SET_FILTER_TUNING", dict(cfg_patch)))
+    def set_stability_profile(self, profile: str):
+        self.send(("SET_STABILITY_PROFILE", str(profile)))
+    def set_nav_goal(self, x: float, y: float):
+        self.send(("SET_NAV_GOAL", {"x": float(x), "y": float(y)}))
+    def clear_nav_goal(self):
+        self.send("CLEAR_NAV_GOAL")
+    def start_localize_only(self):
+        self.send("START_LOCALIZE_ONLY")
+    def stop_localize_only(self):
+        self.send("STOP_LOCALIZE_ONLY")
+    def record_start(self, base_name: str):
+        self.send(("RECORD_START", {"base_name": str(base_name)}))
+    def record_stop(self, save: bool = True, base_name: str = ""):
+        payload = {"save": bool(save)}
+        if base_name:
+            payload["base_name"] = str(base_name)
+        self.send(("RECORD_STOP", payload))
+    def mission_start(self, waypoints: list[dict]):
+        self.send(("MISSION_START", {"waypoints": list(waypoints)}))
+    def mission_pause(self):
+        self.send("MISSION_PAUSE")
+    def mission_resume(self):
+        self.send("MISSION_RESUME")
+    def mission_stop(self):
+        self.send("MISSION_STOP")
+    def get_self_check(self) -> dict:
+        return dict(self.self_check)
+    def sensor_prior(self, sensor: str, pose: Any, confidence: float = 1.0, timestamp: Optional[float] = None):
+        self.send(
+            (
+                "SENSOR_PRIOR",
+                {
+                    "sensor": str(sensor),
+                    "pose": pose,
+                    "confidence": float(confidence),
+                    "timestamp": float(time.time()) if timestamp is None else float(timestamp),
+                },
+            )
+        )
diff --git a/Lidar/.bak-before-prod-sync/SLAM_worker.py b/Lidar/.bak-before-prod-sync/SLAM_worker.py
new file mode 100644
index 0000000..7325746
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/SLAM_worker.py
@@ -0,0 +1,3849 @@
+# SLAM_worker.py
+from __future__ import annotations
+
+import time
+import queue
+import traceback
+import threading
+import multiprocessing as mp
+from collections import deque
+from pathlib import Path
+from typing import Optional, Any, Dict, List, Tuple
+
+import numpy as np
+
+from SLAM_engine import (
+    EngineConfig,
+    FilterConfig,
+    MapConfig,
+    LocalizationConfig,
+    RuntimeConfig,
+    load_slam_config,
+)
+from SLAM_Validation import run_startup_self_check
+
+
+def _now() -> float:
+    return time.time()
+
+
+def _as_bool(v: Any, default: bool = False) -> bool:
+    if v is None:
+        return default
+    if isinstance(v, bool):
+        return v
+    if isinstance(v, (int, float)):
+        return bool(v)
+    return str(v).strip().lower() in ("1", "true", "yes", "on")
+
+
+def _drain_keep_latest(q: mp.Queue) -> None:
+    try:
+        while True:
+            q.get_nowait()
+    except Exception:
+        return
+
+
+def _safe_put(q: mp.Queue, item: Any, keep_latest: bool = False) -> None:
+    try:
+        if keep_latest:
+            _drain_keep_latest(q)
+        q.put_nowait(item)
+    except Exception:
+        pass
+
+
+def slam_worker(
+    data_q: mp.Queue,
+    status_q: mp.Queue,
+    cmd_q: mp.Queue,
+    eng_cfg: EngineConfig,
+    filt_cfg: FilterConfig,
+    map_cfg: MapConfig,
+    loc_cfg: LocalizationConfig,
+    run_cfg: RuntimeConfig,
+):
+    # suppress noisy warning from ctypes->numpy conversion
+    import warnings
+    warnings.filterwarnings(
+        "ignore",
+        message="A builtin ctypes object gave a PEP3118 format string",
+        category=RuntimeWarning,
+    )
+
+    try:
+        from livox2_python import Livox2
+        from kiss_icp.config import load_config
+        from kiss_icp.pipeline import KissICP
+        from SLAM_Diagnostics import WorkerDiagnostics
+        from SLAM_LocalizationService import LocalizationFrameState
+        from SLAM_Transforms import (
+            apply_transform_points as _apply_transform_points,
+            blend_rigid_tf as _blend_rigid_tf_impl,
+            tf_delta as _tf_delta_impl,
+            tf_from_xyzyaw as _tf_from_xyzyaw_impl,
+            to_world_points as _to_world_points,
+            yaw_deg_from_tf as _yaw_deg_from_tf_impl,
+        )
+
+        from SLAM_Filter import VoxelPersistenceFilter
+        from SLAM_Filter import FilterConfig as VFilterCfg
+        from SLAM_Filter import IndoorMapQualityConfig, IndoorMapQualityFilter
+
+        from SLAM_MAP import StableMapLayer
+        from SLAM_MAP import MapConfig as StableMapCfg
+
+        from SLAM_LoopClosure import LoopClosureConfig, LoopClosureBackend
+        from SLAM_PlaceRecognition import PlaceRecognitionConfig, PlaceRecognitionIndex
+        from SLAM_StateMachine import LocalizationStateConfig, LocalizationStateMachine
+        from SLAM_Submap import SubmapConfig, LocalGlobalSubmapMapper, SubmapCheckpointer
+        from SLAM_Session import SessionMemoryConfig, SessionTransformStore
+        from SLAM_Navigation import NavigationExportConfig, NavigationExporter
+        from SLAM_Fusion import FusionConfig as SensorFusionConfig, SensorPoseFusion
+        from SLAM_NavRuntime import (
+            LiveCostmapConfig,
+            LiveCostmapRuntime,
+            GlobalAStarPlanner,
+            LocalPlannerConfig,
+            LocalReactivePlanner,
+        )
+        from SLAM_Mission import MissionConfig, WaypointMissionManager
+        from SLAM_Safety import SafetyConfig, SafetySupervisor
+
+    except Exception as e:
+        _safe_put(status_q, ("ERROR", f"Import failed: {e}"))
+        return
+
+    # Notable event keys that survive to the fault log; high-frequency
+    # status snapshots (LOC_STATE / MODE / FILTER_TUNING) are excluded so
+    # the log stays readable.
+    _FAULT_LOG_INFO_KEYS = {
+        "LOOP", "MAP_LOCK", "MAP_CLEANUP", "RELOC", "AUTOSAVED", "SAVED",
+        "EXPORT", "NAV_EXPORTED", "WORKFLOW_PROFILE", "AUTOSAVE",
+        "PLACE_RECOG", "RECORD_SAVED", "SUBMAP_MODE", "LOC_MACHINE",
+        "MIN_STABLE_POINTS", "LOOP_MODE",
+    }
+
+    def st(level: str, msg: Any):
+        _safe_put(status_q, (level, msg))
+        # Persist non-routine events to the fault log so post-hoc
+        # diagnostics (drift root-causing, "did loop closure fire") work
+        # without the GUI being open at the time.
+        if diagnostics is None:
+            return
+        try:
+            lvl = str(level).upper().strip()
+            if lvl in ("WARN", "ERROR"):
+                diagnostics.note(f"{lvl}: {msg}")
+            elif lvl == "INFO" and isinstance(msg, dict):
+                for k in msg.keys():
+                    key_up = str(k).upper()
+                    if key_up in _FAULT_LOG_INFO_KEYS:
+                        diagnostics.log_state(key_up, msg.get(k))
+                        break
+        except Exception:
+            pass
+
+    Path(map_cfg.data_folder).mkdir(parents=True, exist_ok=True)
+    diagnostics = None
+    try:
+        fault_log_path = Path(map_cfg.data_folder) / "SLAM_worker_fault.log"
+        diagnostics = WorkerDiagnostics(fault_log_path)
+    except Exception as e:
+        diagnostics = None
+        _safe_put(status_q, ("WARN", f"Diagnostics disabled: {e}"))
+    try:
+        full_cfg = load_slam_config()
+    except Exception:
+        full_cfg = {}
+    loc_cfg_raw = full_cfg.get("localization", {}) if isinstance(full_cfg, dict) else {}
+    guard_cfg = full_cfg.get("mapping_guard", {}) if isinstance(full_cfg, dict) else {}
+    loc_pt2plane_unsafe = _as_bool(
+        (loc_cfg_raw or {}).get("allow_point_to_plane_unsafe", False),
+        False,
+    )
+    loc_use_point_to_plane = _as_bool(
+        (loc_cfg_raw or {}).get("use_point_to_plane", False),
+        False,
+    ) and bool(loc_pt2plane_unsafe)
+    if _as_bool((loc_cfg_raw or {}).get("use_point_to_plane", False), False) and not loc_pt2plane_unsafe:
+        _safe_put(
+            status_q,
+            (
+                "WARN",
+                "Localization point-to-plane disabled for stability. Set localization.allow_point_to_plane_unsafe=true to force it.",
+            ),
+        )
+    loc_live_period_sec = max(0.25, float((loc_cfg_raw or {}).get("live_period_sec", 0.55)))
+    loc_tracking_window_enabled = _as_bool(
+        (loc_cfg_raw or {}).get("tracking_window_enabled", True),
+        True,
+    )
+    loc_tracking_window_radius_m = max(
+        2.0,
+        float((loc_cfg_raw or {}).get("tracking_window_radius_m", 8.0)),
+    )
+    loc_tracking_window_min_ref_points = max(
+        120,
+        int((loc_cfg_raw or {}).get("tracking_window_min_ref_points", 450)),
+    )
+    loc_tracking_window_max_ref_points = max(
+        int(loc_tracking_window_min_ref_points),
+        int((loc_cfg_raw or {}).get("tracking_window_max_ref_points", 140000)),
+    )
+    loc_global_reloc_anchor_voxel_m = max(
+        0.4,
+        float((loc_cfg_raw or {}).get("global_reloc_anchor_voxel_m", 1.4)),
+    )
+    loc_global_reloc_max_anchors = max(
+        8,
+        int((loc_cfg_raw or {}).get("global_reloc_max_anchors", 24)),
+    )
+    loc_global_reloc_yaw_step_deg = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("global_reloc_yaw_step_deg", 60.0)),
+            15.0,
+            120.0,
+        )
+    )
+    loc_global_reloc_corr_mult = max(
+        1.2,
+        float((loc_cfg_raw or {}).get("global_reloc_corr_mult", 1.9)),
+    )
+    loc_global_reloc_coarse_iter = max(
+        4,
+        int((loc_cfg_raw or {}).get("global_reloc_coarse_iter", 8)),
+    )
+    loc_global_reloc_refine_iter = max(
+        8,
+        int((loc_cfg_raw or {}).get("global_reloc_refine_iter", 20)),
+    )
+    loc_global_reloc_min_corr = max(
+        15,
+        int((loc_cfg_raw or {}).get("global_reloc_min_corr", 25)),
+    )
+    loc_bidir_enabled = _as_bool((loc_cfg_raw or {}).get("bidirectional_check_enabled", True), True)
+    loc_bidir_min_ratio = float(
+        np.clip(float((loc_cfg_raw or {}).get("bidirectional_min_ratio", 0.18)), 0.01, 1.0)
+    )
+    loc_bidir_max_rmse = max(0.05, float((loc_cfg_raw or {}).get("bidirectional_max_rmse", 0.55)))
+    loc_guess_ttl_sec = max(2.0, float((loc_cfg_raw or {}).get("approx_guess_ttl_sec", 60.0)))
+    loc_guess_crop_radius_m = max(
+        2.0,
+        float((loc_cfg_raw or {}).get("approx_guess_crop_radius_m", 9.0)),
+    )
+    loc_guess_default_z_m = float((loc_cfg_raw or {}).get("approx_guess_default_z_m", 0.0))
+    loc_guess_strict_local = _as_bool(
+        (loc_cfg_raw or {}).get("approx_guess_strict_local", True),
+        True,
+    )
+    loc_guess_local_radius_m = max(
+        1.0,
+        min(
+            float(loc_guess_crop_radius_m),
+            float((loc_cfg_raw or {}).get("approx_guess_local_radius_m", 4.5)),
+        ),
+    )
+    loc_guess_global_fallback_after_failures = max(
+        1,
+        int((loc_cfg_raw or {}).get("approx_guess_global_fallback_after_failures", 6)),
+    )
+    loc_guess_yaw_step_deg = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("approx_guess_yaw_step_deg", 30.0)),
+            10.0,
+            120.0,
+        )
+    )
+    loc_guess_max_start_offset_m = max(
+        1.0,
+        float((loc_cfg_raw or {}).get("approx_guess_max_start_offset_m", 6.0)),
+    )
+    loc_guess_bootstrap_min_hits = max(
+        1,
+        int((loc_cfg_raw or {}).get("approx_guess_bootstrap_min_hits", 2)),
+    )
+    loc_guess_bootstrap_min_fitness = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("approx_guess_bootstrap_min_fitness", 0.34)),
+            0.0,
+            1.0,
+        )
+    )
+    loc_guess_bootstrap_min_bidir = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("approx_guess_bootstrap_min_bidir", 0.24)),
+            0.0,
+            1.0,
+        )
+    )
+    loc_live_track_strict = _as_bool(
+        (loc_cfg_raw or {}).get("live_track_strict", True),
+        True,
+    )
+    loc_live_track_min_fit = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("live_track_min_fitness", 0.42)),
+            0.0,
+            1.0,
+        )
+    )
+    loc_live_track_min_bidir = float(
+        np.clip(
+            float((loc_cfg_raw or {}).get("live_track_min_bidir", 0.28)),
+            0.0,
+            1.0,
+        )
+    )
+    loc_live_track_max_step_trans_m = max(
+        0.02,
+        float((loc_cfg_raw or {}).get("live_track_max_step_translation_m", 0.20)),
+    )
+    loc_live_track_max_step_rot_deg = max(
+        0.5,
+        float((loc_cfg_raw or {}).get("live_track_max_step_rotation_deg", 12.0)),
+    )
+    loc_live_stack_enabled = _as_bool((loc_cfg_raw or {}).get("live_stack_enabled", True), True)
+    loc_live_stack_frames = max(1, int((loc_cfg_raw or {}).get("live_stack_frames", 8)))
+    loc_live_stack_voxel_m = max(
+        0.04,
+        float((loc_cfg_raw or {}).get("live_stack_voxel_m", max(0.06, float(loc_cfg.voxel_localize) * 0.7))),
+    )
+    loc_live_stack_max_points = max(800, int((loc_cfg_raw or {}).get("live_stack_max_points", 22000)))
+    loc_live_stack_min_frames = max(1, int((loc_cfg_raw or {}).get("live_stack_min_frames", 2)))
+
+    vis_cfg = full_cfg.get("localization_visualization", {}) if isinstance(full_cfg, dict) else {}
+    loc_vis_enabled = _as_bool((vis_cfg or {}).get("enabled", True), True)
+    loc_vis_period_sec = max(0.10, float((vis_cfg or {}).get("period_sec", 0.35)))
+    loc_vis_voxel_m = max(0.08, float((vis_cfg or {}).get("voxel_m", 0.24)))
+    loc_vis_max_points = max(4000, int((vis_cfg or {}).get("max_points", 28000)))
+    loc_vis_local_radius_m = max(2.0, float((vis_cfg or {}).get("local_radius_m", 12.0)))
+    loc_vis_match_dist_m = max(0.03, float((vis_cfg or {}).get("match_dist_m", 0.30)))
+    loc_vis_near_dist_m = max(loc_vis_match_dist_m + 0.02, float((vis_cfg or {}).get("near_dist_m", 0.60)))
+    loc_vis_confirm_frames = max(1, int((vis_cfg or {}).get("confirm_frames", 3)))
+    loc_vis_decay_per_frame = max(1, int((vis_cfg or {}).get("decay_per_frame", 1)))
+
+    lidar = None
+    slam = None
+    filt = None
+    stable = None
+
+    loop_cfg = LoopClosureConfig.from_dict(full_cfg.get("loop_closure", {}))
+    loop_backend = LoopClosureBackend(loop_cfg)
+
+    loc_state_cfg = LocalizationStateConfig.from_dict(full_cfg.get("state_machine", {}))
+    loc_state = LocalizationStateMachine(loc_state_cfg)
+
+    session_cfg = SessionMemoryConfig.from_dict(full_cfg.get("session_memory", {}))
+    session_store = SessionTransformStore(map_cfg.data_folder, session_cfg)
+
+    nav_cfg = NavigationExportConfig.from_dict(full_cfg.get("navigation_export", {}))
+    nav_exporter = NavigationExporter(nav_cfg, map_cfg.data_folder)
+    nav_rt_cfg = LiveCostmapConfig.from_dict(full_cfg.get("navigation_runtime", full_cfg.get("navigation_export", {})))
+    nav_runtime = LiveCostmapRuntime(nav_rt_cfg)
+    nav_global_planner = GlobalAStarPlanner(blocked_cost=int(nav_rt_cfg.blocked_cost))
+    nav_local_planner = LocalReactivePlanner(LocalPlannerConfig.from_dict(full_cfg.get("local_planner", {})))
+
+    fusion_cfg = SensorFusionConfig.from_dict(full_cfg.get("fusion", {}))
+    sensor_fusion = SensorPoseFusion(fusion_cfg)
+
+    mission_cfg = MissionConfig.from_dict(full_cfg.get("mission", {}))
+    mission = WaypointMissionManager(mission_cfg)
+
+    safety_cfg = SafetyConfig.from_dict(full_cfg.get("safety", {}))
+    safety = SafetySupervisor(safety_cfg)
+
+    map_quality_cfg = IndoorMapQualityConfig.from_dict(full_cfg.get("map_quality", {}))
+    map_quality = IndoorMapQualityFilter(map_quality_cfg)
+
+    submap_cfg = SubmapConfig.from_dict(full_cfg.get("submap_mapping", {}))
+    submap_mapper = LocalGlobalSubmapMapper(submap_cfg)
+    submap_mode_enabled = bool(submap_cfg.enabled)
+    _submap_raw = full_cfg.get("submap_mapping", {}) or {}
+    _ckpt_enabled = _as_bool(_submap_raw.get("checkpoint_enabled", True), True)
+    _ckpt_interval = max(10.0, float(_submap_raw.get("checkpoint_interval_sec", 60.0)))
+    submap_ckpt = SubmapCheckpointer(str(map_cfg.data_folder), interval_s=_ckpt_interval) if _ckpt_enabled else None
+    if submap_ckpt is not None and submap_mode_enabled:
+        if submap_ckpt.load_into(submap_mapper):
+            st("INFO", f"Submap checkpoint restored from {map_cfg.data_folder}")
+    place_cfg = PlaceRecognitionConfig.from_dict(full_cfg.get("place_recognition", {}))
+    place_recog = PlaceRecognitionIndex(place_cfg, Path(__file__).resolve().parent)
+
+    livox_dbg_cfg = full_cfg.get("livox_debug", {}) or {}
+    livox_debug_enabled = _as_bool(livox_dbg_cfg.get("enabled", False), False)
+    livox_print_every_n = max(1, int(livox_dbg_cfg.get("print_every_n_frames", 20)))
+
+    pose_guard_enabled = _as_bool((guard_cfg or {}).get("enabled", True), True)
+    pose_guard_max_trans = max(0.08, float((guard_cfg or {}).get("max_frame_translation_m", 0.45)))
+    pose_guard_max_rot_deg = max(2.0, float((guard_cfg or {}).get("max_frame_rotation_deg", 30.0)))
+    pose_guard_ref_dt = max(0.01, float((guard_cfg or {}).get("reference_dt_sec", 0.06)))
+
+    map_lock_cfg = full_cfg.get("mapping_lock", {}) if isinstance(full_cfg, dict) else {}
+    map_lock_enabled = _as_bool((map_lock_cfg or {}).get("enabled", True), True)
+    map_lock_period_sec = max(0.5, float((map_lock_cfg or {}).get("period_sec", 1.5)))
+    map_lock_voxel_m = max(0.05, float((map_lock_cfg or {}).get("voxel_m", 0.22)))
+    map_lock_max_corr_m = max(0.3, float((map_lock_cfg or {}).get("max_corr_m", 1.2)))
+    map_lock_min_stable_pts = max(200, int((map_lock_cfg or {}).get("min_stable_points", 700)))
+    map_lock_accept_fitness = float(np.clip(float((map_lock_cfg or {}).get("accept_fitness", 0.28)), 0.0, 1.0))
+    map_lock_accept_rmse = max(0.05, float((map_lock_cfg or {}).get("accept_rmse", 0.40)))
+    map_lock_max_trans_m = max(0.02, float((map_lock_cfg or {}).get("max_step_translation_m", 0.35)))
+    map_lock_max_rot_deg = max(1.0, float((map_lock_cfg or {}).get("max_step_rotation_deg", 15.0)))
+    map_lock_apply_damping = _as_bool((map_lock_cfg or {}).get("apply_damping", True), True)
+    map_lock_apply_alpha_t = float(np.clip(float((map_lock_cfg or {}).get("apply_alpha_translation", 0.42)), 0.05, 1.0))
+    map_lock_apply_alpha_r = float(np.clip(float((map_lock_cfg or {}).get("apply_alpha_rotation", 0.50)), 0.05, 1.0))
+    map_lock_apply_max_trans_m = max(0.01, float((map_lock_cfg or {}).get("apply_max_translation_m", 0.08)))
+    map_lock_apply_max_rot_deg = max(0.5, float((map_lock_cfg or {}).get("apply_max_rotation_deg", 5.0)))
+    map_lock_window_radius_m = max(0.0, float((map_lock_cfg or {}).get("window_radius_m", 8.0)))
+    map_lock_min_live_pts = max(60, int((map_lock_cfg or {}).get("min_live_points", 140)))
+    map_lock_icp_max_iter = max(5, int((map_lock_cfg or {}).get("icp_max_iter", 20)))
+    map_lock_block_on_reject = _as_bool((map_lock_cfg or {}).get("block_on_reject", True), True)
+    map_lock_retry_after_sec = float((map_lock_cfg or {}).get("retry_after_reject_sec", 0.20))
+    map_lock_retry_after_sec = float(np.clip(map_lock_retry_after_sec, 0.05, max(0.05, map_lock_period_sec)))
+    map_lock_block_on_severe = _as_bool((map_lock_cfg or {}).get("block_on_severe_reject", True), True)
+    map_lock_severe_fit = float(np.clip(float((map_lock_cfg or {}).get("severe_fit", 0.14)), 0.0, 1.0))
+    map_lock_severe_rmse = max(0.10, float((map_lock_cfg or {}).get("severe_rmse", 0.75)))
+    map_lock_severe_trans_m = max(
+        float(map_lock_max_trans_m),
+        float((map_lock_cfg or {}).get("severe_trans_m", 0.45)),
+    )
+    map_lock_severe_rot_deg = max(
+        float(map_lock_max_rot_deg),
+        float((map_lock_cfg or {}).get("severe_rot_deg", 22.0)),
+    )
+    rotate_cfg = full_cfg.get("rotate_guard", {}) if isinstance(full_cfg, dict) else {}
+    rotate_mode_enabled = _as_bool((rotate_cfg or {}).get("enabled", True), True)
+    rotate_in_place_max_trans_m = max(0.01, float((rotate_cfg or {}).get("in_place_max_trans_m", 0.05)))
+    rotate_in_place_min_rot_deg = max(0.5, float((rotate_cfg or {}).get("in_place_min_rot_deg", 4.0)))
+    rotate_pose_guard_max_rot_deg = max(
+        float(pose_guard_max_rot_deg),
+        float((rotate_cfg or {}).get("pose_guard_max_rot_deg", 34.0)),
+    )
+    rotate_skip_continuity = _as_bool((rotate_cfg or {}).get("skip_continuity", True), True)
+    rotate_map_lock_period_sec = max(0.05, float((rotate_cfg or {}).get("map_lock_period_sec", 0.25)))
+    rotate_map_lock_accept_fitness = float(
+        np.clip(float((rotate_cfg or {}).get("map_lock_accept_fitness", max(0.05, map_lock_accept_fitness - 0.06))), 0.0, 1.0)
+    )
+    rotate_map_lock_accept_rmse = max(
+        float(map_lock_accept_rmse),
+        float((rotate_cfg or {}).get("map_lock_accept_rmse", map_lock_accept_rmse + 0.10)),
+    )
+    rotate_map_lock_max_step_rot_deg = max(
+        float(map_lock_max_rot_deg),
+        float((rotate_cfg or {}).get("map_lock_max_step_rot_deg", 14.0)),
+    )
+    rotate_map_lock_apply_alpha_t = float(
+        np.clip(
+            float((rotate_cfg or {}).get("map_lock_apply_alpha_translation", max(0.05, map_lock_apply_alpha_t * 0.55))),
+            0.03,
+            1.0,
+        )
+    )
+    rotate_map_lock_apply_alpha_r = float(
+        np.clip(
+            float((rotate_cfg or {}).get("map_lock_apply_alpha_rotation", max(0.05, map_lock_apply_alpha_r * 0.70))),
+            0.03,
+            1.0,
+        )
+    )
+    rotate_map_lock_apply_max_trans_m = max(
+        0.005,
+        float((rotate_cfg or {}).get("map_lock_apply_max_translation_m", max(0.01, map_lock_apply_max_trans_m * 0.45))),
+    )
+    rotate_map_lock_apply_max_rot_deg = max(
+        0.5,
+        float((rotate_cfg or {}).get("map_lock_apply_max_rotation_deg", max(1.0, map_lock_apply_max_rot_deg * 0.70))),
+    )
+    rotate_freeze_mapping = _as_bool((rotate_cfg or {}).get("freeze_mapping_while_rotating", True), True)
+    rotate_freeze_min_stable_pts = max(0, int((rotate_cfg or {}).get("freeze_min_stable_points", 250)))
+    rotate_map_lock_block_on_reject = _as_bool((rotate_cfg or {}).get("block_on_reject", False), False)
+    rotate_map_lock_block_on_severe = _as_bool((rotate_cfg or {}).get("block_on_severe", True), True)
+    continuity_cfg = full_cfg.get("continuity_guard", {}) if isinstance(full_cfg, dict) else {}
+    continuity_enabled = _as_bool((continuity_cfg or {}).get("enabled", True), True)
+    continuity_voxel_m = max(0.05, float((continuity_cfg or {}).get("voxel_m", 0.24)))
+    continuity_min_points = max(40, int((continuity_cfg or {}).get("min_points", 120)))
+    continuity_match_radius_m = max(0.08, float((continuity_cfg or {}).get("match_radius_m", 0.45)))
+    continuity_min_inlier_ratio = float(np.clip(float((continuity_cfg or {}).get("min_inlier_ratio", 0.35)), 0.01, 1.0))
+    continuity_max_median_m = max(0.05, float((continuity_cfg or {}).get("max_median_m", 0.30)))
+    continuity_max_p90_m = max(0.08, float((continuity_cfg or {}).get("max_p90_m", 0.60)))
+    continuity_recover_after_rejects = max(2, int((continuity_cfg or {}).get("recover_after_rejects", 10)))
+    cleanup_cfg = full_cfg.get("map_cleanup", {}) if isinstance(full_cfg, dict) else {}
+    cleanup_enabled = _as_bool((cleanup_cfg or {}).get("enabled", True), True)
+    cleanup_period_sec = max(0.5, float((cleanup_cfg or {}).get("period_sec", 1.8)))
+    cleanup_voxel_m = max(0.05, float((cleanup_cfg or {}).get("voxel_m", 0.16)))
+    cleanup_cluster_radius_m = max(0.12, float((cleanup_cfg or {}).get("cluster_radius_m", 0.42)))
+    cleanup_min_cluster_points = max(20, int((cleanup_cfg or {}).get("min_cluster_points", 120)))
+    cleanup_keep_largest_n = max(1, int((cleanup_cfg or {}).get("keep_largest_n", 1)))
+    cleanup_strict_largest_only = _as_bool((cleanup_cfg or {}).get("strict_largest_only", True), True)
+    cleanup_min_total_points = max(150, int((cleanup_cfg or {}).get("min_total_points", 800)))
+
+    autosave_cfg = full_cfg.get("autosave", {}) or {}
+    autosave_enabled = _as_bool(autosave_cfg.get("enabled", False), False)
+    autosave_interval_sec = max(5.0, float(autosave_cfg.get("interval_sec", 90.0)))
+    autosave_base_name = str(autosave_cfg.get("base_name", "autosave_map"))
+    last_autosave_t = 0.0
+
+    self_check_report = run_startup_self_check(full_cfg, Path(__file__).resolve().parent)
+
+    connected = False
+    mapping_enabled = False
+    localize_only_enabled = False
+    worker_mode = "IDLE"
+    running = True
+    state_lock = threading.RLock()
+
+    # localization
+    ref_map_path: Optional[str] = None
+
+    # publish control
+    pub_hz = float(run_cfg.publish_hz)
+    last_pub = 0.0
+
+    # keyframe gating
+    last_key_pose = None
+    last_frame_t = 0.0
+    last_guard_pose = None
+    last_guard_t = 0.0
+    guard_reject_count = 0
+    guard_last_warn_t = 0.0
+    odom_to_map_transform = np.eye(4, dtype=np.float64)
+    map_lock_last_t = 0.0
+    map_lock_accept_count = 0
+    map_lock_reject_count = 0
+    map_lock_last_warn_t = 0.0
+    continuity_prev_pts = None
+    continuity_reject_count = 0
+    continuity_consecutive_rejects = 0
+    continuity_last_warn_t = 0.0
+    rotate_freeze_count = 0
+    rotate_last_warn_t = 0.0
+    cleanup_last_t = 0.0
+    cleanup_removed_total = 0
+    cleanup_last_info: Dict[str, Any] = {"status": "na"}
+
+    # localization cadence/state
+    last_localize_t = 0.0
+    last_localize_points = 0
+    last_localize_transform = np.eye(4, dtype=np.float64)
+    last_localize_confidence = 0.0
+    ref_pcd_cache = None
+    ref_points_cache: Optional[np.ndarray] = None
+    ref_nav_points_cache: Optional[np.ndarray] = None
+    ref_nav_cache_src_n: int = 0
+    ref_cache_path: Optional[str] = None
+    slam_to_ref_transform = np.eye(4, dtype=np.float64)
+    slam_to_ref_valid = False
+    approx_pose_guess_tf: Optional[np.ndarray] = None
+    approx_pose_guess_until_t: float = 0.0
+    approx_pose_guess_fail_count: int = 0
+    approx_pose_guess_success_count: int = 0
+    ref_match_points_cache: Optional[np.ndarray] = None
+    ref_match_confirm_hits: Optional[np.ndarray] = None
+    ref_match_last_pub_t: float = 0.0
+    ref_match_last_stats: Dict[str, Any] = {}
+    loc_live_stack: deque[np.ndarray] = deque(maxlen=int(loc_live_stack_frames))
+    loc_frames = LocalizationFrameState()
+
+    # runtime point-density control (LOW/MEDIUM/HIGH)
+    base_stride = max(1, int(eng_cfg.pre_downsample_stride))
+    density_mode = "MEDIUM"
+    live_stride = base_stride
+    runtime_filter_voxel = float(filt_cfg.voxel_size)
+    runtime_filter_hit_threshold = int(max(1, filt_cfg.hits_threshold))
+    runtime_filter_decay = float(filt_cfg.strict_sec if filt_cfg.use_strict else filt_cfg.window_sec)
+    stability_profile = "BALANCED"
+
+    # telemetry/performance
+    perf_input_count = 0
+    perf_publish_count = 0
+    perf_last_rate_t = _now()
+    perf_last_cpu_wall_t = _now()
+    perf_last_cpu_proc_t = time.process_time()
+    perf_input_fps = 0.0
+    perf_publish_fps = 0.0
+    perf_icp_ms = 0.0
+    perf_cpu_pct = 0.0
+    perf_queue_lag = 0
+    perf_stable_growth = 0.0
+    perf_prev_stable_count = 0
+    perf_prev_stable_t = _now()
+
+    # recording / replay capture
+    recording_enabled = False
+    recording_frames: List[np.ndarray] = []
+    recording_poses: List[np.ndarray] = []
+    recording_base_name = "slam_recording"
+    recording_max_frames = max(100, int((full_cfg.get("replay", {}) or {}).get("max_record_frames", 6000)))
+    recording_drop_count = 0
+
+    # nav runtime / planner / mission
+    nav_goal_xy: Optional[Tuple[float, float]] = None
+    nav_last_update_t = 0.0
+    nav_last_plan_t = 0.0
+    nav_plan_period_sec = max(0.15, float((full_cfg.get("navigation_runtime", {}) or {}).get("plan_period_sec", 0.35)))
+    nav_path_world: List[Tuple[float, float]] = []
+    latest_live_pts: Optional[np.ndarray] = None
+    latest_live_localize_pts: Optional[np.ndarray] = None
+    raw_points_q: queue.Queue = queue.Queue(maxsize=max(2, int(run_cfg.frame_queue_maxsize)))
+
+    def _sync_loc_vars_from_service() -> None:
+        nonlocal odom_to_map_transform, last_localize_transform, last_localize_confidence
+        nonlocal slam_to_ref_transform, slam_to_ref_valid
+        odom_to_map_transform = np.array(loc_frames.odom_to_map, dtype=np.float64, copy=True)
+        last_localize_transform = np.array(loc_frames.last_alignment, dtype=np.float64, copy=True)
+        last_localize_confidence = float(loc_frames.confidence)
+        slam_to_ref_transform = np.array(loc_frames.odom_to_ref, dtype=np.float64, copy=True)
+        slam_to_ref_valid = bool(loc_frames.ref_valid)
+
+    def _worker_diag_state() -> Dict[str, Any]:
+        return {
+            "connected": bool(connected),
+            "mapping_enabled": bool(mapping_enabled),
+            "localize_only_enabled": bool(localize_only_enabled),
+            "worker_mode": str(worker_mode),
+            "ref_map_path": str(ref_map_path) if ref_map_path else "",
+            "last_localize_points": int(last_localize_points),
+            "last_localize_t": float(last_localize_t),
+            "density_mode": str(density_mode),
+            "stability_profile": str(stability_profile),
+            "submap_enabled": bool(submap_mode_enabled),
+            "loop_closure_enabled": bool(loop_cfg.enabled),
+            "loc_frames": loc_frames.snapshot(),
+        }
+
+    def _clear_raw_points_queue() -> None:
+        try:
+            while True:
+                raw_points_q.get_nowait()
+        except Exception:
+            return
+
+    # ---------------- init stack ----------------
+    def init_slam_stack():
+        nonlocal slam, filt, stable
+
+        cfg = load_config(config_file=None, max_range=float(eng_cfg.max_range))
+        cfg.mapping.voxel_size = float(eng_cfg.slam_voxel_size)
+        slam = KissICP(cfg)
+
+        vf_cfg = VFilterCfg(
+            voxel_size=float(runtime_filter_voxel),
+            hit_threshold=int(runtime_filter_hit_threshold),
+            decay_seconds=float(runtime_filter_decay),
+            max_voxels=int(filt_cfg.max_voxels),
+        )
+        filt = VoxelPersistenceFilter(vf_cfg)
+
+        sm_cfg = StableMapCfg(
+            display_voxel=float(map_cfg.display_voxel),
+            save_voxel=float(map_cfg.save_voxel),
+            data_folder=str(map_cfg.data_folder),
+            save_extension=str(map_cfg.save_extension),
+        )
+        stable = StableMapLayer(sm_cfg)
+
+    def _run_locked(fn, *args, **kwargs):
+        with state_lock:
+            return fn(*args, **kwargs)
+
+    def reset_all():
+        nonlocal mapping_enabled, localize_only_enabled, worker_mode, last_key_pose
+        nonlocal last_guard_pose, last_guard_t, guard_reject_count, guard_last_warn_t
+        nonlocal last_frame_t
+        nonlocal odom_to_map_transform, map_lock_last_t, map_lock_accept_count, map_lock_reject_count, map_lock_last_warn_t
+        nonlocal continuity_prev_pts, continuity_reject_count, continuity_consecutive_rejects, continuity_last_warn_t
+        nonlocal rotate_freeze_count, rotate_last_warn_t
+        nonlocal cleanup_last_t, cleanup_removed_total, cleanup_last_info
+        nonlocal last_localize_t, last_localize_points, last_localize_transform, last_localize_confidence
+        nonlocal ref_pcd_cache, ref_points_cache, ref_nav_points_cache, ref_nav_cache_src_n, ref_cache_path, slam_to_ref_transform, slam_to_ref_valid
+        nonlocal approx_pose_guess_tf, approx_pose_guess_until_t, approx_pose_guess_fail_count, approx_pose_guess_success_count
+        nonlocal ref_match_points_cache, ref_match_confirm_hits, ref_match_last_pub_t, ref_match_last_stats
+        nonlocal loc_live_stack
+        nonlocal last_autosave_t
+        nonlocal perf_input_count, perf_publish_count, perf_last_rate_t
+        nonlocal perf_last_cpu_wall_t, perf_last_cpu_proc_t
+        nonlocal perf_input_fps, perf_publish_fps, perf_icp_ms, perf_cpu_pct
+        nonlocal perf_queue_lag, perf_stable_growth, perf_prev_stable_count, perf_prev_stable_t
+        nonlocal recording_enabled, recording_frames, recording_poses, recording_drop_count
+        nonlocal nav_goal_xy, nav_last_update_t, nav_last_plan_t, nav_path_world, latest_live_pts, latest_live_localize_pts
+        init_slam_stack()
+        mapping_enabled = False
+        localize_only_enabled = False
+        worker_mode = "IDLE"
+        last_frame_t = 0.0
+        last_key_pose = None
+        last_guard_pose = None
+        last_guard_t = 0.0
+        guard_reject_count = 0
+        guard_last_warn_t = 0.0
+        loc_frames.reset()
+        _sync_loc_vars_from_service()
+        map_lock_last_t = 0.0
+        map_lock_accept_count = 0
+        map_lock_reject_count = 0
+        map_lock_last_warn_t = 0.0
+        continuity_prev_pts = None
+        continuity_reject_count = 0
+        continuity_consecutive_rejects = 0
+        continuity_last_warn_t = 0.0
+        rotate_freeze_count = 0
+        rotate_last_warn_t = 0.0
+        cleanup_last_t = 0.0
+        cleanup_removed_total = 0
+        cleanup_last_info = {"status": "na"}
+        last_localize_t = 0.0
+        last_localize_points = 0
+        ref_pcd_cache = None
+        ref_points_cache = None
+        ref_nav_points_cache = None
+        ref_nav_cache_src_n = 0
+        ref_cache_path = None
+        approx_pose_guess_tf = None
+        approx_pose_guess_until_t = 0.0
+        approx_pose_guess_fail_count = 0
+        approx_pose_guess_success_count = 0
+        ref_match_points_cache = None
+        ref_match_confirm_hits = None
+        ref_match_last_pub_t = 0.0
+        ref_match_last_stats = {}
+        loc_live_stack.clear()
+        loop_backend.reset()
+        loc_state.reset()
+        submap_mapper.reset()
+        if submap_ckpt is not None:
+            submap_ckpt.delete()
+        sensor_fusion.reset()
+        _imu_integrator.reset()
+        mission.reset()
+        nav_runtime.reset()
+        last_autosave_t = 0.0
+        perf_input_count = 0
+        perf_publish_count = 0
+        perf_last_rate_t = _now()
+        perf_last_cpu_wall_t = _now()
+        perf_last_cpu_proc_t = time.process_time()
+        perf_input_fps = 0.0
+        perf_publish_fps = 0.0
+        perf_icp_ms = 0.0
+        perf_cpu_pct = 0.0
+        perf_queue_lag = 0
+        perf_stable_growth = 0.0
+        perf_prev_stable_count = 0
+        perf_prev_stable_t = _now()
+        recording_enabled = False
+        recording_frames = []
+        recording_poses = []
+        recording_drop_count = 0
+        nav_goal_xy = None
+        nav_last_update_t = 0.0
+        nav_last_plan_t = 0.0
+        nav_path_world = []
+        latest_live_pts = None
+        latest_live_localize_pts = None
+        _clear_raw_points_queue()
+        st("INFO", "RESET done: SLAM + Filter + Stable map cleared. (no save)")
+
+    init_slam_stack()
+
+    # ---------------- helpers ----------------
+    def height_colors_fast(pts: np.ndarray, alpha: float = 0.9) -> np.ndarray:
+        if pts is None or len(pts) == 0:
+            return np.zeros((0, 4), dtype=np.float32)
+        z = pts[:, 2]
+        colors = np.zeros((len(pts), 4), dtype=np.float32)
+        colors[:, 3] = alpha
+        norm = np.clip((z + 1.5) / 3.0, 0, 1)
+        colors[:, 0] = norm
+        colors[:, 1] = 1.0 - np.abs(norm - 0.5) * 2.0
+        colors[:, 2] = 1.0 - norm
+        return colors
+
+    def pose_delta_ok(pose, last_pose) -> bool:
+        if last_pose is None:
+            return True
+        dp = pose[:3, 3] - last_pose[:3, 3]
+        trans = float(np.linalg.norm(dp))
+        R = pose[:3, :3] @ last_pose[:3, :3].T
+        ang = float(np.degrees(np.arccos(np.clip((np.trace(R) - 1) / 2, -1, 1))))
+        return (trans >= float(eng_cfg.keyframe_min_translation_m)) or (ang >= float(eng_cfg.keyframe_min_rotation_deg))
+
+    def to_world_points(points_sensor: np.ndarray, pose: Optional[np.ndarray]) -> np.ndarray:
+        """
+        Convert sensor-frame points to world frame using pose (world_T_sensor).
+        Falls back to input points when pose is unavailable/invalid.
+        """
+        return _to_world_points(points_sensor, pose)
+
+    def apply_transform_points(points: Optional[np.ndarray], transform: np.ndarray) -> Optional[np.ndarray]:
+        return _apply_transform_points(points, transform)
+
+    def _tf_delta(prev_tf: np.ndarray, new_tf: np.ndarray) -> Tuple[float, float]:
+        return _tf_delta_impl(prev_tf, new_tf)
+
+    def _blend_rigid_tf(prev_tf: np.ndarray, new_tf: np.ndarray, alpha_t: float, alpha_r: float) -> np.ndarray:
+        return _blend_rigid_tf_impl(prev_tf, new_tf, alpha_t, alpha_r)
+
+    def _yaw_deg_from_tf(tf: np.ndarray) -> float:
+        return _yaw_deg_from_tf_impl(tf)
+
+    def _tf_from_xyzyaw(x: float, y: float, z: float, yaw_deg: float) -> np.ndarray:
+        return _tf_from_xyzyaw_impl(x, y, z, yaw_deg)
+
+    def voxel_downsample_np(points: np.ndarray, voxel_m: float) -> np.ndarray:
+        pts = np.asarray(points, dtype=np.float32)
+        if pts.ndim != 2 or pts.shape[1] != 3 or len(pts) == 0:
+            return np.zeros((0, 3), dtype=np.float32)
+        v = float(voxel_m)
+        if v <= 0.0:
+            return pts
+        keys = np.floor(pts / v).astype(np.int32)
+        _, first_idx = np.unique(keys, axis=0, return_index=True)
+        return pts[np.sort(first_idx)]
+
+    def _rigid_fit_svd(src_pts: np.ndarray, dst_pts: np.ndarray) -> Optional[np.ndarray]:
+        """Estimate rigid transform (dst = T * src) with SVD."""
+        if src_pts is None or dst_pts is None:
+            return None
+        if len(src_pts) < 3 or len(dst_pts) < 3:
+            return None
+        a = np.asarray(src_pts, dtype=np.float64)
+        b = np.asarray(dst_pts, dtype=np.float64)
+        if a.shape != b.shape or a.ndim != 2 or a.shape[1] != 3:
+            return None
+        c_a = np.mean(a, axis=0)
+        c_b = np.mean(b, axis=0)
+        aa = a - c_a
+        bb = b - c_b
+        H = aa.T @ bb
+        try:
+            U, _, Vt = np.linalg.svd(H, full_matrices=False)
+        except Exception:
+            return None
+        R = Vt.T @ U.T
+        if np.linalg.det(R) < 0:
+            Vt[2, :] *= -1.0
+            R = Vt.T @ U.T
+        t = c_b - (R @ c_a)
+        T = np.eye(4, dtype=np.float64)
+        T[:3, :3] = R
+        T[:3, 3] = t
+        return T
+
+    def _icp_point_to_point_np(
+        src_pts: np.ndarray,
+        tgt_pts: np.ndarray,
+        init_tf: Optional[np.ndarray],
+        max_corr_m: float,
+        max_iter: int,
+        min_corr: int = 30,
+    ) -> Dict[str, Any]:
+        """
+        Pure NumPy/SciPy point-to-point ICP to avoid Open3D native crashes.
+        Returns {transform, fitness, rmse, inliers}.
+        """
+        src0 = np.asarray(src_pts, dtype=np.float64)
+        tgt = np.asarray(tgt_pts, dtype=np.float64)
+        if src0.ndim != 2 or tgt.ndim != 2 or src0.shape[1] != 3 or tgt.shape[1] != 3:
+            return {"transform": np.eye(4, dtype=np.float64), "fitness": 0.0, "rmse": 9e9, "inliers": 0}
+        if len(src0) < 3 or len(tgt) < 3:
+            return {"transform": np.eye(4, dtype=np.float64), "fitness": 0.0, "rmse": 9e9, "inliers": 0}
+
+        try:
+            from scipy.spatial import cKDTree
+        except Exception:
+            return {"transform": np.eye(4, dtype=np.float64), "fitness": 0.0, "rmse": 9e9, "inliers": 0}
+
+        T = np.eye(4, dtype=np.float64) if init_tf is None else np.asarray(init_tf, dtype=np.float64).copy()
+        if T.shape != (4, 4):
+            T = np.eye(4, dtype=np.float64)
+        tree = cKDTree(tgt)
+        max_corr = float(max(0.05, max_corr_m))
+        min_corr_n = int(max(10, min_corr))
+        last_rmse = 9e9
+        last_inliers = 0
+
+        for _ in range(int(max(1, max_iter))):
+            src_w = (src0 @ T[:3, :3].T) + T[:3, 3]
+            d, idx = tree.query(src_w, k=1)
+            keep = np.isfinite(d) & (d <= max_corr)
+            n_in = int(np.count_nonzero(keep))
+            if n_in < min_corr_n:
+                break
+            src_corr = src_w[keep]
+            tgt_corr = tgt[np.asarray(idx[keep], dtype=np.int32)]
+            delta = _rigid_fit_svd(src_corr, tgt_corr)
+            if delta is None:
+                break
+            T = np.asarray(delta, dtype=np.float64) @ T
+            prev_rmse = float(last_rmse)
+            rmse = float(np.sqrt(np.mean(np.square(d[keep]))))
+            last_rmse = rmse
+            last_inliers = n_in
+            # Tiny improvement => converged.
+            if abs(prev_rmse - rmse) < 1e-5:
+                break
+
+        fitness = float(last_inliers) / max(1.0, float(len(src0)))
+        return {"transform": T, "fitness": float(fitness), "rmse": float(last_rmse), "inliers": int(last_inliers)}
+
+    def _bidir_match_stats(
+        src_pts: np.ndarray,
+        tgt_pts: np.ndarray,
+        transform: np.ndarray,
+        max_corr_m: float,
+    ) -> Dict[str, Any]:
+        stats = {
+            "ok": False,
+            "live_to_ref_inlier": 0.0,
+            "ref_to_live_inlier": 0.0,
+            "bidir_inlier": 0.0,
+            "bidir_rmse": 9e9,
+        }
+        try:
+            from scipy.spatial import cKDTree
+        except Exception:
+            return stats
+        try:
+            src = np.asarray(src_pts, dtype=np.float64)
+            tgt = np.asarray(tgt_pts, dtype=np.float64)
+            tf = np.asarray(transform, dtype=np.float64)
+            if src.ndim != 2 or tgt.ndim != 2 or src.shape[1] != 3 or tgt.shape[1] != 3:
+                return stats
+            if len(src) < 8 or len(tgt) < 8 or tf.shape != (4, 4):
+                return stats
+            src_w = (src @ tf[:3, :3].T) + tf[:3, 3]
+            max_corr = max(0.05, float(max_corr_m))
+            tree_t = cKDTree(tgt)
+            d_st, _ = tree_t.query(src_w, k=1)
+            in_st = np.isfinite(d_st) & (d_st <= max_corr)
+            in_st_ratio = float(np.count_nonzero(in_st)) / max(1.0, float(len(src_w)))
+
+            tree_s = cKDTree(src_w)
+            d_ts, _ = tree_s.query(tgt, k=1)
+            in_ts = np.isfinite(d_ts) & (d_ts <= max_corr)
+            in_ts_ratio = float(np.count_nonzero(in_ts)) / max(1.0, float(len(tgt)))
+
+            inlier_mix = np.concatenate([d_st[in_st], d_ts[in_ts]], axis=0)
+            rmse = float(np.sqrt(np.mean(np.square(inlier_mix)))) if len(inlier_mix) > 0 else 9e9
+
+            stats.update(
+                {
+                    "ok": True,
+                    "live_to_ref_inlier": float(in_st_ratio),
+                    "ref_to_live_inlier": float(in_ts_ratio),
+                    "bidir_inlier": float(min(in_st_ratio, in_ts_ratio)),
+                    "bidir_rmse": float(rmse),
+                }
+            )
+            return stats
+        except Exception:
+            return stats
+
+    def _build_ref_match_visual(
+        live_ref_pts: Optional[np.ndarray],
+        pose_ref: Optional[np.ndarray],
+        now_t: float,
+    ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Dict[str, Any]]:
+        nonlocal ref_match_points_cache, ref_match_confirm_hits, ref_match_last_pub_t, ref_match_last_stats
+        stats: Dict[str, Any] = {"enabled": bool(loc_vis_enabled), "updated": False}
+        if not loc_vis_enabled:
+            ref_match_last_stats = stats
+            return None, None, stats
+        if ref_points_cache is None or len(ref_points_cache) == 0:
+            stats["reason"] = "no_ref"
+            ref_match_last_stats = stats
+            return None, None, stats
+        if (float(now_t) - float(ref_match_last_pub_t)) < float(loc_vis_period_sec):
+            return None, None, dict(ref_match_last_stats)
+
+        ref_match_last_pub_t = float(now_t)
+        if ref_match_points_cache is None or ref_match_confirm_hits is None:
+            ref_ds = voxel_downsample_np(np.asarray(ref_points_cache, dtype=np.float32), float(loc_vis_voxel_m))
+            if len(ref_ds) > int(loc_vis_max_points):
+                step = max(2, int(np.ceil(float(len(ref_ds)) / float(loc_vis_max_points))))
+                ref_ds = ref_ds[::step]
+            ref_match_points_cache = np.asarray(ref_ds, dtype=np.float32)
+            ref_match_confirm_hits = np.zeros((len(ref_match_points_cache),), dtype=np.int16)
+
+        pts_ref = np.asarray(ref_match_points_cache, dtype=np.float32)
+        if len(pts_ref) == 0:
+            stats["reason"] = "empty_ref_sample"
+            ref_match_last_stats = stats
+            return pts_ref, np.zeros((0, 4), dtype=np.float32), stats
+
+        local_mask = np.ones((len(pts_ref),), dtype=bool)
+        center = None
+        if pose_ref is not None:
+            pose_np = np.asarray(pose_ref, dtype=np.float64)
+            if pose_np.shape == (4, 4):
+                center = np.asarray(pose_np[:3, 3], dtype=np.float32)
+        if center is not None and np.isfinite(center).all():
+            rel = pts_ref - center.reshape((1, 3))
+            d2 = np.einsum("ij,ij->i", rel, rel)
+            local_mask = d2 <= (float(loc_vis_local_radius_m) * float(loc_vis_local_radius_m))
+
+        colors = np.zeros((len(pts_ref), 4), dtype=np.float32)
+        colors[:, :3] = np.array([0.45, 0.45, 0.45], dtype=np.float32)
+        colors[:, 3] = 0.16
+
+        live = None if live_ref_pts is None else np.asarray(live_ref_pts, dtype=np.float32)
+        matched = np.zeros((len(pts_ref),), dtype=bool)
+        near = np.zeros((len(pts_ref),), dtype=bool)
+        unmatched = np.zeros((len(pts_ref),), dtype=bool)
+        try:
+            if live is not None and live.ndim == 2 and live.shape[1] == 3 and len(live) >= 20 and np.any(local_mask):
+                from scipy.spatial import cKDTree
+                tree = cKDTree(np.asarray(live, dtype=np.float64))
+                idx = np.where(local_mask)[0]
+                d, _ = tree.query(np.asarray(pts_ref[idx], dtype=np.float64), k=1)
+                d = np.asarray(d, dtype=np.float64)
+                match_idx = idx[d <= float(loc_vis_match_dist_m)]
+                near_idx = idx[(d > float(loc_vis_match_dist_m)) & (d <= float(loc_vis_near_dist_m))]
+                unmatch_idx = idx[d > float(loc_vis_near_dist_m)]
+                matched[match_idx] = True
+                near[near_idx] = True
+                unmatched[unmatch_idx] = True
+        except Exception as _vis_exc:
+            st("WARN", f"[loc_vis] match visual update failed: {_vis_exc}")
+
+        if ref_match_confirm_hits is None or len(ref_match_confirm_hits) != len(pts_ref):
+            ref_match_confirm_hits = np.zeros((len(pts_ref),), dtype=np.int16)
+        hits = np.asarray(ref_match_confirm_hits, dtype=np.int16)
+        hits[matched] = np.clip(hits[matched] + 1, 0, 32000)
+        decay_mask = local_mask & (~matched)
+        if np.any(decay_mask):
+            hits[decay_mask] = np.maximum(0, hits[decay_mask] - int(loc_vis_decay_per_frame))
+        confirmed = local_mask & (hits >= int(loc_vis_confirm_frames))
+        ref_match_confirm_hits = hits
+
+        colors[unmatched, :3] = np.array([0.95, 0.15, 0.15], dtype=np.float32)
+        colors[unmatched, 3] = 0.22
+        colors[near, :3] = np.array([0.96, 0.86, 0.16], dtype=np.float32)
+        colors[near, 3] = 0.24
+        colors[matched, :3] = np.array([0.15, 0.85, 0.20], dtype=np.float32)
+        colors[matched, 3] = 0.24
+        colors[confirmed, :3] = np.array([0.05, 1.00, 0.28], dtype=np.float32)
+        colors[confirmed, 3] = 0.34
+
+        local_n = int(np.count_nonzero(local_mask))
+        stats.update(
+            {
+                "updated": True,
+                "ref_points": int(len(pts_ref)),
+                "local_points": int(local_n),
+                "matched": int(np.count_nonzero(matched)),
+                "near": int(np.count_nonzero(near)),
+                "unmatched": int(np.count_nonzero(unmatched)),
+                "confirmed": int(np.count_nonzero(confirmed)),
+                "matched_ratio": float(np.count_nonzero(matched)) / max(1.0, float(local_n)),
+            }
+        )
+        ref_match_last_stats = dict(stats)
+        return pts_ref, colors, stats
+
+    def compute_map_lock_correction(
+        points_sensor: np.ndarray,
+        pose_map: Optional[np.ndarray],
+        now_t: float,
+        overrides: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[Optional[np.ndarray], Dict[str, Any]]:
+        nonlocal map_lock_last_t
+        info: Dict[str, Any] = {"status": "skip", "ran": False}
+        ov = overrides if isinstance(overrides, dict) else {}
+        period_sec_local = max(0.05, float(ov.get("period_sec", map_lock_period_sec)))
+        max_corr_local = max(0.20, float(ov.get("max_corr_m", map_lock_max_corr_m)))
+        accept_fit_local = float(np.clip(float(ov.get("accept_fitness", map_lock_accept_fitness)), 0.0, 1.0))
+        accept_rmse_local = max(0.05, float(ov.get("accept_rmse", map_lock_accept_rmse)))
+        max_step_trans_local = max(0.01, float(ov.get("max_step_translation_m", map_lock_max_trans_m)))
+        max_step_rot_local = max(0.5, float(ov.get("max_step_rotation_deg", map_lock_max_rot_deg)))
+        icp_iter_local = max(5, int(ov.get("icp_max_iter", map_lock_icp_max_iter)))
+
+        if (not map_lock_enabled) or (pose_map is None):
+            return None, info
+        if (now_t - float(map_lock_last_t)) < float(period_sec_local):
+            return None, info
+        map_lock_last_t = float(now_t)
+
+        try:
+            stable_all = stable.get_points()
+        except Exception:
+            stable_all = None
+        n_stable = 0 if stable_all is None else int(len(stable_all))
+        if stable_all is None or n_stable < int(map_lock_min_stable_pts):
+            info.update({"status": "skip", "reason": "few_stable", "stable_points": int(n_stable)})
+            return None, info
+
+        src_sensor = np.asarray(points_sensor, dtype=np.float32)
+        if src_sensor.ndim != 2 or src_sensor.shape[1] != 3 or len(src_sensor) < int(map_lock_min_live_pts):
+            info.update({"status": "skip", "reason": "few_live", "live_points": int(len(src_sensor))})
+            return None, info
+
+        pose_np = np.asarray(pose_map, dtype=np.float64)
+        if pose_np.shape != (4, 4):
+            info.update({"status": "skip", "reason": "bad_pose"})
+            return None, info
+
+        src_world = to_world_points(src_sensor, pose_np)
+        tgt_world = np.asarray(stable_all, dtype=np.float32)
+
+        if float(map_lock_window_radius_m) > 0.0 and len(tgt_world) > 0:
+            center = np.asarray(pose_np[:3, 3], dtype=np.float32)
+            rel = tgt_world - center
+            d2 = np.einsum("ij,ij->i", rel, rel)
+            win2 = float(map_lock_window_radius_m) * float(map_lock_window_radius_m)
+            keep = d2 <= win2
+            min_local = max(120, int(map_lock_min_stable_pts) // 3)
+            if int(np.count_nonzero(keep)) >= min_local:
+                tgt_world = tgt_world[keep]
+
+        src_ds = voxel_downsample_np(src_world, float(map_lock_voxel_m))
+        tgt_ds = voxel_downsample_np(tgt_world, float(map_lock_voxel_m))
+        if len(src_ds) < 60 or len(tgt_ds) < 120:
+            info.update(
+                {
+                    "status": "skip",
+                    "reason": "few_downsampled",
+                    "src_points": int(len(src_ds)),
+                    "tgt_points": int(len(tgt_ds)),
+                }
+            )
+            return None, info
+
+        # Robust in-process point-to-point ICP (avoids Open3D native crashes seen in map-lock path).
+        try:
+            from scipy.spatial import cKDTree
+        except Exception:
+            info.update({"status": "skip", "reason": "scipy_missing"})
+            return None, info
+
+        src_icp = np.asarray(src_ds, dtype=np.float64)
+        tgt_icp = np.asarray(tgt_ds, dtype=np.float64)
+        tree = cKDTree(tgt_icp)
+        max_corr = float(max_corr_local)
+        min_corr = max(40, int(0.15 * len(src_icp)))
+
+        def _rigid_fit(src_pts: np.ndarray, dst_pts: np.ndarray) -> Optional[np.ndarray]:
+            if len(src_pts) < 3 or len(dst_pts) < 3:
+                return None
+            c_src = np.mean(src_pts, axis=0)
+            c_dst = np.mean(dst_pts, axis=0)
+            src0 = src_pts - c_src
+            dst0 = dst_pts - c_dst
+            H = src0.T @ dst0
+            try:
+                U, _, Vt = np.linalg.svd(H, full_matrices=False)
+            except Exception:
+                return None
+            R = Vt.T @ U.T
+            if np.linalg.det(R) < 0:
+                Vt[-1, :] *= -1.0
+                R = Vt.T @ U.T
+            t = c_dst - (R @ c_src)
+            T = np.eye(4, dtype=np.float64)
+            T[:3, :3] = R
+            T[:3, 3] = t
+            return T
+
+        corr = np.eye(4, dtype=np.float64)
+        converged = False
+        for _ in range(int(icp_iter_local)):
+            src_w = (src_icp @ corr[:3, :3].T) + corr[:3, 3]
+            dist, idx = tree.query(src_w, k=1)
+            mask = np.isfinite(dist) & (dist <= max_corr)
+            if int(np.count_nonzero(mask)) < min_corr:
+                break
+            src_in = src_w[mask]
+            dst_in = tgt_icp[idx[mask]]
+            delta = _rigid_fit(src_in, dst_in)
+            if delta is None:
+                break
+            corr = delta @ corr
+            step_t = float(np.linalg.norm(delta[:3, 3]))
+            step_r = float(
+                np.degrees(
+                    np.arccos(np.clip((np.trace(delta[:3, :3]) - 1.0) * 0.5, -1.0, 1.0))
+                )
+            )
+            if step_t < 0.001 and step_r < 0.08:
+                converged = True
+                break
+
+        src_final = (src_icp @ corr[:3, :3].T) + corr[:3, 3]
+        dist_f, _ = tree.query(src_final, k=1)
+        mask_f = np.isfinite(dist_f) & (dist_f <= max_corr)
+        inliers = int(np.count_nonzero(mask_f))
+        if inliers < min_corr:
+            info.update(
+                {
+                    "ran": True,
+                    "status": "rejected",
+                    "reason": "few_inliers",
+                    "src_points": int(len(src_icp)),
+                    "tgt_points": int(len(tgt_icp)),
+                }
+            )
+            return None, info
+        fit = float(inliers) / float(max(1, len(src_icp)))
+        rmse = float(np.sqrt(np.mean((dist_f[mask_f]) ** 2)))
+        dpos = float(np.linalg.norm(corr[:3, 3]))
+        drot = float(np.degrees(np.arccos(np.clip((np.trace(corr[:3, :3]) - 1.0) * 0.5, -1.0, 1.0))))
+
+        ok = bool(
+            fit >= float(accept_fit_local)
+            and rmse <= float(accept_rmse_local)
+            and dpos <= float(max_step_trans_local)
+            and drot <= float(max_step_rot_local)
+        )
+        info.update(
+            {
+                "ran": True,
+                "status": "accepted" if ok else "rejected",
+                "fitness": fit,
+                "rmse": rmse,
+                "dpos_m": dpos,
+                "drot_deg": drot,
+                "src_points": int(len(src_ds)),
+                "tgt_points": int(len(tgt_ds)),
+                "converged": bool(converged),
+                "inliers": int(inliers),
+                "period_sec": float(period_sec_local),
+                "accept_fit": float(accept_fit_local),
+                "accept_rmse": float(accept_rmse_local),
+            }
+        )
+        return (corr if ok else None), info
+
+    def check_frame_continuity(points_world: np.ndarray) -> Tuple[bool, Dict[str, Any], Optional[np.ndarray]]:
+        info: Dict[str, Any] = {"enabled": bool(continuity_enabled), "status": "skip"}
+        pts = np.asarray(points_world, dtype=np.float32)
+        pts_ds = voxel_downsample_np(pts, float(continuity_voxel_m))
+        if (not continuity_enabled) or pts_ds is None or len(pts_ds) < int(continuity_min_points):
+            info.update({"status": "skip", "reason": "disabled_or_few_points", "points": int(len(pts_ds))})
+            return True, info, pts_ds
+        if continuity_prev_pts is None or len(continuity_prev_pts) < int(continuity_min_points):
+            info.update({"status": "bootstrap", "points": int(len(pts_ds))})
+            return True, info, pts_ds
+
+        try:
+            from scipy.spatial import cKDTree
+            tree = cKDTree(np.asarray(continuity_prev_pts, dtype=np.float64))
+            dist, _ = tree.query(np.asarray(pts_ds, dtype=np.float64), k=1)
+        except Exception as e:
+            info.update({"status": "skip", "reason": f"tree_error:{e}"})
+            return True, info, pts_ds
+
+        mask = np.isfinite(dist)
+        if int(np.count_nonzero(mask)) < int(continuity_min_points):
+            info.update({"status": "skip", "reason": "few_matches"})
+            return True, info, pts_ds
+        d = dist[mask]
+        inlier_ratio = float(np.mean(d <= float(continuity_match_radius_m)))
+        median_d = float(np.median(d))
+        p90_d = float(np.percentile(d, 90.0))
+        ok = bool(
+            inlier_ratio >= float(continuity_min_inlier_ratio)
+            and median_d <= float(continuity_max_median_m)
+            and p90_d <= float(continuity_max_p90_m)
+        )
+        info.update(
+            {
+                "status": "ok" if ok else "reject",
+                "points": int(len(pts_ds)),
+                "inlier_ratio": float(inlier_ratio),
+                "median_m": float(median_d),
+                "p90_m": float(p90_d),
+            }
+        )
+        return ok, info, pts_ds
+
+    def cleanup_map_islands(points_world: np.ndarray) -> Tuple[np.ndarray, Dict[str, Any]]:
+        pts = np.asarray(points_world, dtype=np.float32)
+        info: Dict[str, Any] = {
+            "enabled": bool(cleanup_enabled),
+            "status": "skip",
+            "before": int(len(pts)),
+            "after": int(len(pts)),
+            "removed": 0,
+        }
+        if (not cleanup_enabled) or len(pts) < int(cleanup_min_total_points):
+            info["reason"] = "disabled_or_few_points"
+            return pts, info
+
+        pts_ds = voxel_downsample_np(pts, float(cleanup_voxel_m))
+        if len(pts_ds) < int(cleanup_min_cluster_points):
+            info["reason"] = "few_downsampled"
+            return pts, info
+
+        try:
+            from scipy.spatial import cKDTree
+            tree = cKDTree(np.asarray(pts_ds, dtype=np.float64))
+        except Exception as e:
+            info["reason"] = f"tree_error:{e}"
+            return pts, info
+
+        n = int(len(pts_ds))
+        visited = np.zeros((n,), dtype=bool)
+        comp_id = np.full((n,), -1, dtype=np.int32)
+        sizes: List[int] = []
+
+        for i in range(n):
+            if visited[i]:
+                continue
+            q = [i]
+            visited[i] = True
+            members: List[int] = []
+            while q:
+                j = int(q.pop())
+                members.append(j)
+                neigh = tree.query_ball_point(pts_ds[j], r=float(cleanup_cluster_radius_m))
+                for k in neigh:
+                    kk = int(k)
+                    if not visited[kk]:
+                        visited[kk] = True
+                        q.append(kk)
+            cid = len(sizes)
+            sizes.append(len(members))
+            for m in members:
+                comp_id[m] = int(cid)
+
+        if not sizes:
+            info["reason"] = "no_components"
+            return pts, info
+
+        order = sorted(range(len(sizes)), key=lambda c: int(sizes[c]), reverse=True)
+        keep_ids = set(order[: int(cleanup_keep_largest_n)])
+        if not cleanup_strict_largest_only:
+            for cid, sz in enumerate(sizes):
+                if int(sz) >= int(cleanup_min_cluster_points):
+                    keep_ids.add(int(cid))
+        if len(keep_ids) == len(sizes):
+            info.update({"status": "ok", "components": int(len(sizes)), "kept_components": int(len(keep_ids))})
+            return pts, info
+
+        # Map original points to nearest downsampled component and keep only selected components.
+        _, nn = tree.query(np.asarray(pts, dtype=np.float64), k=1)
+        keep_mask = np.isin(comp_id[np.asarray(nn, dtype=np.int32)], np.asarray(list(keep_ids), dtype=np.int32))
+        cleaned = pts[keep_mask]
+        removed = int(len(pts) - len(cleaned))
+        info.update(
+            {
+                "status": "ok",
+                "components": int(len(sizes)),
+                "kept_components": int(len(keep_ids)),
+                "before": int(len(pts)),
+                "after": int(len(cleaned)),
+                "removed": int(removed),
+            }
+        )
+        return cleaned, info
+
+    def push_loc_state() -> None:
+        st("INFO", {"LOC_STATE": loc_state.snapshot()})
+        if diagnostics is not None:
+            diagnostics.log_state("LOC_STATE", _worker_diag_state())
+
+    def push_worker_mode() -> None:
+        st(
+            "INFO",
+            {
+                "MODE": {
+                    "mode": str(worker_mode),
+                    "mapping": bool(mapping_enabled),
+                    "localize_only": bool(localize_only_enabled),
+                }
+            },
+        )
+        if diagnostics is not None:
+            diagnostics.log_state("MODE", _worker_diag_state())
+
+    def _place_profile_allowed() -> bool:
+        profile = str(stability_profile).upper().strip()
+        return bool(place_recog.profile_allowed(profile))
+
+    # ---------------- localization ----------------
+    def do_localize(
+        force: bool = False,
+        source: str = "MANUAL",
+        src_override: Optional[np.ndarray] = None,
+    ) -> Optional[Dict[str, Any]]:
+        nonlocal ref_map_path, last_localize_t, last_localize_points, last_localize_transform, last_localize_confidence
+        nonlocal ref_pcd_cache, ref_points_cache, ref_nav_points_cache, ref_nav_cache_src_n, ref_cache_path
+        nonlocal slam_to_ref_transform, slam_to_ref_valid
+        nonlocal approx_pose_guess_tf, approx_pose_guess_until_t, approx_pose_guess_fail_count, approx_pose_guess_success_count
+        now = _now()
+        src_tag = str(source).upper().strip()
+        use_live_src = src_override is not None
+        if not loc_cfg.enabled:
+            if force:
+                st("ERROR", "LOCALIZE disabled in config.")
+                loc_state.update(None, now)
+                push_loc_state()
+            return None
+        if not ref_map_path:
+            if force:
+                st("ERROR", "LOCALIZE: load a reference map first.")
+                loc_state.update(None, now)
+                push_loc_state()
+            return None
+
+        try:
+            period = max(0.1, float(loc_cfg.period_sec))
+            if use_live_src:
+                # Live relocalization should be frequent but not every frame to avoid jitter.
+                period = min(period, float(loc_live_period_sec))
+            if (not force) and ((now - last_localize_t) < period):
+                return None
+
+            req_loc = int(loc_cfg.min_points_for_localize)
+            if use_live_src:
+                cur = np.asarray(src_override, dtype=np.float32)
+                cur_n = int(len(cur))
+                req_live = max(80, min(req_loc, 1000))
+                if cur_n < req_live:
+                    if force:
+                        st("ERROR", f"LOCALIZE: not enough live points ({cur_n}/{req_live}).")
+                    loc_state.update(None, now)
+                    push_loc_state()
+                    return None
+            else:
+                cur = stable.get_save_points()
+                cur_n = 0 if cur is None else int(len(cur))
+                if cur is None or cur_n < req_loc:
+                    if force:
+                        st("ERROR", f"LOCALIZE: not enough stable points ({cur_n}/{req_loc}). Start mapping first.")
+                        loc_state.update(None, now)
+                        push_loc_state()
+                    return None
+                if (not force) and ((cur_n - int(last_localize_points)) < int(loc_cfg.min_new_points)):
+                    return None
+
+            import open3d as o3d
+
+            if ref_pcd_cache is None or ref_cache_path != ref_map_path:
+                ref_pcd_cache = o3d.io.read_point_cloud(ref_map_path)
+                if hasattr(ref_pcd_cache, "points"):
+                    ref_points_cache = np.asarray(ref_pcd_cache.points, dtype=np.float32).copy()
+                else:
+                    ref_points_cache = np.asarray(ref_pcd_cache, dtype=np.float32).reshape((-1, 3)).copy()
+                ref_nav_points_cache = None
+                ref_nav_cache_src_n = 0
+                ref_cache_path = ref_map_path
+            if ref_points_cache is None:
+                if hasattr(ref_pcd_cache, "points"):
+                    ref_points_cache = np.asarray(ref_pcd_cache.points, dtype=np.float32).copy()
+                else:
+                    ref_points_cache = np.asarray(ref_pcd_cache, dtype=np.float32).reshape((-1, 3)).copy()
+                ref_nav_points_cache = None
+                ref_nav_cache_src_n = 0
+            ref_np_full = np.asarray(ref_points_cache, dtype=np.float32)
+            if len(ref_np_full) < int(loc_cfg.min_points_for_localize):
+                if force:
+                    st("ERROR", "LOCALIZE: reference map has too few points.")
+                    loc_state.update(None, now)
+                    push_loc_state()
+                return None
+            if bool(place_cfg.enabled) and _place_profile_allowed():
+                if (not bool(place_recog.index_ready)) or (str(place_recog.ref_path or "") != str(ref_map_path)):
+                    pr_info = place_recog.build(ref_np_full, ref_map_path)
+                    st("INFO", {"PLACE_RECOG": pr_info})
+
+            cur_np_full = np.asarray(cur, dtype=np.float32)
+            if cur_np_full.ndim != 2 or cur_np_full.shape[1] != 3:
+                loc_state.update(None, now)
+                push_loc_state()
+                return None
+
+            # Once localized, keep tracking in a local window around the last pose
+            # to avoid jumping to similar structures elsewhere in a large map.
+            tracking_locked = bool(
+                use_live_src
+                and slam_to_ref_valid
+                and src_tag not in ("ON_LOAD", "RECOVERY")
+                and str(loc_state.state).upper().strip() in ("TRACKING", "DEGRADED")
+            )
+            ref_np_track = np.asarray(ref_np_full, dtype=np.float32)
+            tracking_window_used = False
+            tracking_window_reason = "full_ref"
+            if tracking_locked and loc_tracking_window_enabled and len(ref_np_track) > 0:
+                try:
+                    center = np.asarray(last_localize_transform[:3, 3], dtype=np.float32)
+                    if np.isfinite(center).all():
+                        rel = ref_np_track - center
+                        d2 = np.einsum("ij,ij->i", rel, rel)
+                        win2 = float(loc_tracking_window_radius_m) * float(loc_tracking_window_radius_m)
+                        keep = d2 <= win2
+                        n_keep = int(np.count_nonzero(keep))
+                        if n_keep >= int(loc_tracking_window_min_ref_points):
+                            ref_np_track = ref_np_track[keep]
+                            tracking_window_used = True
+                            tracking_window_reason = "window"
+                        else:
+                            tracking_window_reason = "window_too_few"
+                except Exception:
+                    tracking_window_reason = "window_error"
+            if len(ref_np_track) > int(loc_tracking_window_max_ref_points):
+                step = max(2, int(np.ceil(float(len(ref_np_track)) / float(loc_tracking_window_max_ref_points))))
+                ref_np_track = ref_np_track[::step]
+                if tracking_window_used:
+                    tracking_window_reason = "window_decimated"
+                else:
+                    tracking_window_reason = "full_ref_decimated"
+
+            voxel = float(loc_cfg.voxel_localize)
+            if use_live_src:
+                voxel = min(voxel, 0.16)
+            max_corr = max(0.5, float(loc_cfg.max_corr_mult) * voxel)
+            if use_live_src:
+                max_corr = min(max_corr, 0.95)
+            accept_fit_target = float(loc_cfg.accept_fitness)
+            accept_rmse_target = float(loc_cfg.accept_rmse)
+            guess_bootstrap = bool(
+                approx_pose_guess_tf is not None
+                and float(now) <= float(approx_pose_guess_until_t)
+                and (not slam_to_ref_valid)
+            )
+            guess_strict_local_active = bool(
+                guess_bootstrap and (not slam_to_ref_valid) and bool(loc_guess_strict_local)
+            )
+            if use_live_src:
+                # Slightly stricter acceptance in live mode to reduce wobble while turning.
+                accept_fit_target = max(0.34, min(0.98, accept_fit_target + 0.08))
+                accept_rmse_target = min(accept_rmse_target, 0.26)
+            if guess_bootstrap:
+                # If user provided approximate location, allow slight environmental changes.
+                max_corr = min(1.15, max_corr * 1.15)
+                accept_fit_target = max(0.22, accept_fit_target - 0.04)
+                accept_rmse_target = max(accept_rmse_target, 0.34)
+
+            init = np.array(last_localize_transform, dtype=np.float64, copy=True)
+            approx_guess_used = False
+            approx_guess_active = bool(guess_bootstrap)
+            if approx_guess_active and not slam_to_ref_valid:
+                init = np.asarray(approx_pose_guess_tf, dtype=np.float64).copy()
+                approx_guess_used = True
+                # Bootstrap with local reference crop around approximate click.
+                try:
+                    center_guess = np.asarray(init[:3, 3], dtype=np.float32)
+                    rel = ref_np_track - center_guess.reshape((1, 3))
+                    d2 = np.einsum("ij,ij->i", rel, rel)
+                    local_r2 = float(loc_guess_local_radius_m) * float(loc_guess_local_radius_m)
+                    wide_r2 = float(loc_guess_crop_radius_m) * float(loc_guess_crop_radius_m)
+                    keep_local = d2 <= local_r2
+                    n_local = int(np.count_nonzero(keep_local))
+                    min_local_n = int(loc_tracking_window_min_ref_points)
+                    if n_local >= min_local_n:
+                        ref_np_track = ref_np_track[keep_local]
+                        tracking_window_used = True
+                        tracking_window_reason = "approx_local_crop"
+                    else:
+                        keep_wide = d2 <= wide_r2
+                        n_wide = int(np.count_nonzero(keep_wide))
+                        min_wide_n = max(120, min_local_n // 2) if guess_strict_local_active else min_local_n
+                        if n_wide >= min_wide_n:
+                            ref_np_track = ref_np_track[keep_wide]
+                            tracking_window_used = True
+                            tracking_window_reason = "approx_wide_crop"
+                        else:
+                            tracking_window_reason = "approx_crop_too_few"
+                except Exception:
+                    tracking_window_reason = "approx_crop_error"
+
+            src_d = voxel_downsample_np(cur_np_full, voxel)
+            tgt_d = voxel_downsample_np(ref_np_track, voxel)
+            if len(src_d) < 40 or len(tgt_d) < 40:
+                if force:
+                    st("ERROR", "LOCALIZE: not enough downsampled points for ICP.")
+                loc_state.update(None, now)
+                push_loc_state()
+                return None
+
+            # Coarse-to-fine ICP improves convergence when the initial offset is large.
+            coarse_voxel = max(voxel, 0.35)
+            src_c = voxel_downsample_np(cur_np_full, coarse_voxel)
+            coarse_tgt_src = ref_np_track if (tracking_locked or approx_guess_used) else ref_np_full
+            tgt_c = voxel_downsample_np(coarse_tgt_src, coarse_voxel)
+            def _run_local_icp_chain(init_tf: np.ndarray) -> Dict[str, Any]:
+                seed = np.asarray(init_tf, dtype=np.float64).copy()
+                if len(src_c) > 40 and len(tgt_c) > 40:
+                    coarse = _icp_point_to_point_np(
+                        src_c,
+                        tgt_c,
+                        seed,
+                        max_corr_m=max_corr * 1.6,
+                        max_iter=max(15, int(loc_cfg.icp_max_iter) // 2),
+                        min_corr=30,
+                    )
+                    seed = np.array(coarse.get("transform", seed), dtype=np.float64, copy=True)
+                return _icp_point_to_point_np(
+                    src_d,
+                    tgt_d,
+                    seed,
+                    max_corr_m=max_corr,
+                    max_iter=max(10, int(loc_cfg.icp_max_iter)),
+                    min_corr=35,
+                )
+
+            reg = _run_local_icp_chain(init)
+            yaw_hypotheses = 0
+            place_query: Dict[str, Any] = {"ok": False, "reason": "disabled"}
+            if approx_guess_active and not slam_to_ref_valid and float(loc_guess_yaw_step_deg) < 180.0:
+                base_yaw = float(_yaw_deg_from_tf(init))
+                yaw_offsets = np.arange(-180.0, 180.0, float(loc_guess_yaw_step_deg), dtype=np.float64)
+                best_reg = dict(reg)
+                for yaw_off in yaw_offsets:
+                    if abs(float(yaw_off)) < 1e-6:
+                        continue
+                    cand = np.asarray(init, dtype=np.float64).copy()
+                    rz = _tf_from_xyzyaw(0.0, 0.0, 0.0, base_yaw + float(yaw_off))
+                    cand[:3, :3] = np.asarray(rz[:3, :3], dtype=np.float64)
+                    rr = _run_local_icp_chain(cand)
+                    yaw_hypotheses += 1
+                    better = (float(rr.get("fitness", 0.0)) > float(best_reg.get("fitness", 0.0))) or (
+                        float(rr.get("fitness", 0.0)) == float(best_reg.get("fitness", 0.0))
+                        and float(rr.get("rmse", 9e9)) < float(best_reg.get("rmse", 9e9))
+                    )
+                    if better:
+                        best_reg = rr
+                reg = dict(best_reg)
+            used_recovery = False
+
+            def accepted(rr: Dict[str, Any]) -> bool:
+                return bool(
+                    float(rr.get("fitness", 0.0)) >= float(accept_fit_target)
+                    and float(rr.get("rmse", 9e9)) <= float(accept_rmse_target)
+                )
+
+            # Global relocalization fallback:
+            # only for bootstrap/lost states, not for normal LIVE tracking.
+            allow_global_reloc = bool(
+                (not slam_to_ref_valid)
+                or src_tag in ("ON_LOAD", "RECOVERY")
+                or str(loc_state.state).upper().strip() in ("LOST", "RECOVERY")
+            )
+            global_reloc_blocked_by_guess = False
+            if guess_strict_local_active and (not slam_to_ref_valid):
+                if int(approx_pose_guess_fail_count) < int(loc_guess_global_fallback_after_failures):
+                    allow_global_reloc = False
+                    global_reloc_blocked_by_guess = True
+            if (not accepted(reg)) and allow_global_reloc:
+                src_np = np.asarray(src_d, dtype=np.float64)
+                # Search against the full reference map to recover from wrong initial position.
+                tgt_global = voxel_downsample_np(ref_np_full, max(float(voxel), 0.24))
+                tgt_np = np.asarray(tgt_global, dtype=np.float64)
+                if len(src_np) > 20 and len(tgt_np) > 40:
+                    c_src = np.mean(src_np, axis=0)
+                    anchors = np.zeros((0, 3), dtype=np.float64)
+                    use_place_retrieval = bool(place_cfg.enabled and _place_profile_allowed())
+                    if use_place_retrieval:
+                        approx_center = None
+                        if approx_pose_guess_tf is not None:
+                            try:
+                                approx_center = np.asarray(approx_pose_guess_tf[:3, 3], dtype=np.float32)
+                            except Exception:
+                                approx_center = None
+                        place_query = place_recog.query(cur_np_full, approx_center=approx_center)
+                        if bool(place_query.get("ok", False)):
+                            anchors = np.asarray(
+                                [c.get("center", [0.0, 0.0, 0.0]) for c in place_query.get("candidates", [])],
+                                dtype=np.float64,
+                            ).reshape((-1, 3))
+                    if len(anchors) == 0:
+                        anchors = voxel_downsample_np(
+                            np.asarray(tgt_np, dtype=np.float32),
+                            max(float(loc_global_reloc_anchor_voxel_m), float(voxel) * 3.0),
+                        )
+                        anchors = np.asarray(anchors, dtype=np.float64)
+                        if len(anchors) == 0:
+                            anchors = np.asarray([np.mean(tgt_np, axis=0)], dtype=np.float64)
+                        if len(anchors) > int(loc_global_reloc_max_anchors):
+                            step = max(1, int(np.ceil(float(len(anchors)) / float(loc_global_reloc_max_anchors))))
+                            anchors = anchors[::step][: int(loc_global_reloc_max_anchors)]
+
+                    yaw_step = float(loc_global_reloc_yaw_step_deg)
+                    yaw_candidates = list(np.arange(-180.0, 180.0, yaw_step, dtype=np.float64))
+                    if 0.0 not in yaw_candidates:
+                        yaw_candidates.append(0.0)
+
+                    best = dict(reg)
+                    for anchor in anchors:
+                        anchor_v = np.asarray(anchor, dtype=np.float64)
+                        for yaw_deg in yaw_candidates:
+                            yaw = np.deg2rad(float(yaw_deg))
+                            cy = float(np.cos(yaw))
+                            sy = float(np.sin(yaw))
+                            rz = np.array(
+                                [[cy, -sy, 0.0], [sy, cy, 0.0], [0.0, 0.0, 1.0]],
+                                dtype=np.float64,
+                            )
+                            init_global = np.eye(4, dtype=np.float64)
+                            init_global[:3, :3] = rz
+                            init_global[:3, 3] = anchor_v - (rz @ c_src)
+                            rr = _icp_point_to_point_np(
+                                src_np,
+                                tgt_np,
+                                init_global,
+                                max_corr_m=float(max_corr) * float(loc_global_reloc_corr_mult),
+                                max_iter=int(loc_global_reloc_coarse_iter),
+                                min_corr=int(loc_global_reloc_min_corr),
+                            )
+                            better = (float(rr.get("fitness", 0.0)) > float(best.get("fitness", 0.0))) or (
+                                float(rr.get("fitness", 0.0)) == float(best.get("fitness", 0.0))
+                                and float(rr.get("rmse", 9e9)) < float(best.get("rmse", 9e9))
+                            )
+                            if better:
+                                best = rr
+
+                    # Refine the best global candidate.
+                    best_tf = np.asarray(best.get("transform", np.eye(4, dtype=np.float64)), dtype=np.float64)
+                    best_refined = _icp_point_to_point_np(
+                        src_np,
+                        tgt_np,
+                        best_tf,
+                        max_corr_m=float(max_corr) * max(1.2, float(loc_global_reloc_corr_mult) * 0.8),
+                        max_iter=int(loc_global_reloc_refine_iter),
+                        min_corr=max(20, int(loc_global_reloc_min_corr)),
+                    )
+                    if (float(best_refined.get("fitness", 0.0)) > float(best.get("fitness", 0.0))) or (
+                        float(best_refined.get("fitness", 0.0)) == float(best.get("fitness", 0.0))
+                        and float(best_refined.get("rmse", 9e9)) < float(best.get("rmse", 9e9))
+                    ):
+                        best = best_refined
+
+                    if (float(best.get("fitness", 0.0)) > float(reg.get("fitness", 0.0))) or (
+                        float(best.get("fitness", 0.0)) == float(reg.get("fitness", 0.0))
+                        and float(best.get("rmse", 9e9)) < float(reg.get("rmse", 9e9))
+                    ):
+                        reg = best
+                        used_recovery = True
+
+            reg_tf = np.asarray(reg.get("transform", np.eye(4, dtype=np.float64)), dtype=np.float64)
+            fit = float(reg.get("fitness", 0.0))
+            rmse = float(reg.get("rmse", 9e9))
+            acc = accepted(reg)
+            temporal_guard_reject = False
+            bootstrap_pending = False
+            bootstrap_ok = False
+            guess_offset_m = -1.0
+            if approx_guess_active and (approx_pose_guess_tf is not None):
+                try:
+                    guess_center = np.asarray(approx_pose_guess_tf[:3, 3], dtype=np.float64)
+                    guess_offset_m = float(np.linalg.norm(np.asarray(reg_tf[:3, 3], dtype=np.float64) - guess_center))
+                except Exception:
+                    guess_offset_m = -1.0
+                if guess_strict_local_active and guess_offset_m >= 0.0:
+                    if guess_offset_m > float(loc_guess_max_start_offset_m):
+                        acc = False
+            bidir = _bidir_match_stats(src_d, tgt_d, reg_tf, max_corr_m=max_corr)
+            if bool(loc_bidir_enabled) and bool(bidir.get("ok", False)):
+                if (
+                    float(bidir.get("bidir_inlier", 0.0)) < float(loc_bidir_min_ratio)
+                    or float(bidir.get("bidir_rmse", 9e9)) > float(loc_bidir_max_rmse)
+                ):
+                    acc = False
+            if use_live_src and slam_to_ref_valid and src_tag not in ("RECOVERY", "ON_LOAD"):
+                dtr_m, drot_deg = _tf_delta(last_localize_transform, reg_tf)
+                max_step_t = float(loc_live_track_max_step_trans_m) if (localize_only_enabled and loc_live_track_strict) else 0.35
+                max_step_r = float(loc_live_track_max_step_rot_deg) if (localize_only_enabled and loc_live_track_strict) else 22.0
+                if (dtr_m > max_step_t or drot_deg > max_step_r) and (fit < (accept_fit_target + 0.10)):
+                    acc = False
+                    temporal_guard_reject = True
+            if use_live_src and slam_to_ref_valid and src_tag not in ("RECOVERY", "ON_LOAD") and acc:
+                # Keep tracking lock conservative to prevent map-frame drift.
+                if localize_only_enabled and loc_live_track_strict:
+                    live_fit_min = max(float(loc_live_track_min_fit), float(accept_fit_target) + 0.02)
+                else:
+                    live_fit_min = max(0.38, float(accept_fit_target) + 0.04)
+                if fit < live_fit_min:
+                    acc = False
+                    temporal_guard_reject = True
+                elif bool(loc_bidir_enabled) and bool(bidir.get("ok", False)):
+                    if localize_only_enabled and loc_live_track_strict:
+                        live_bidir_min = max(float(loc_live_track_min_bidir), float(loc_bidir_min_ratio))
+                    else:
+                        live_bidir_min = max(0.24, float(loc_bidir_min_ratio) + 0.06)
+                    if float(bidir.get("bidir_inlier", 0.0)) < live_bidir_min:
+                        acc = False
+                        temporal_guard_reject = True
+            if approx_guess_active and (not slam_to_ref_valid):
+                bootstrap_ok = bool(acc)
+                if bootstrap_ok and (fit < float(loc_guess_bootstrap_min_fitness)):
+                    bootstrap_ok = False
+                if bootstrap_ok and bool(bidir.get("ok", False)):
+                    if float(bidir.get("bidir_inlier", 0.0)) < float(loc_guess_bootstrap_min_bidir):
+                        bootstrap_ok = False
+                if bootstrap_ok and guess_offset_m >= 0.0:
+                    if guess_offset_m > float(loc_guess_max_start_offset_m):
+                        bootstrap_ok = False
+                if bootstrap_ok:
+                    approx_pose_guess_success_count += 1
+                    # Seed next bootstrap attempt from this best candidate.
+                    loc_frames.last_alignment = np.array(reg_tf, dtype=np.float64, copy=True)
+                    _sync_loc_vars_from_service()
+                    if int(approx_pose_guess_success_count) < int(loc_guess_bootstrap_min_hits):
+                        bootstrap_pending = True
+                        acc = False
+                else:
+                    approx_pose_guess_success_count = 0
+            fit_floor = max(1e-6, float(loc_state_cfg.lost_fitness))
+            fit_span = max(1e-6, float(accept_fit_target) - fit_floor)
+            fit_score = float(np.clip((fit - fit_floor) / fit_span, 0.0, 1.0))
+            rmse_cap = max(1e-6, float(accept_rmse_target) * 1.5)
+            rmse_score = float(np.clip((rmse_cap - rmse) / rmse_cap, 0.0, 1.0))
+            confidence = float(np.clip(0.62 * fit_score + 0.38 * rmse_score, 0.0, 1.0))
+            if acc:
+                confidence = max(confidence, 0.75)
+            if acc and use_live_src and slam_to_ref_valid and src_tag != "RECOVERY":
+                # Low-pass localization transform in live mode to reduce visual map shake.
+                alpha = 0.45 if fit >= (accept_fit_target + 0.08) else 0.30
+                reg_tf = _blend_rigid_tf(last_localize_transform, reg_tf, alpha_t=alpha, alpha_r=alpha)
+
+            sigma_xy = float(max(0.02, rmse * (1.3 - fit_score)))
+            sigma_z = float(max(0.03, rmse * (1.6 - fit_score)))
+            sigma_yaw = float(max(0.02, (1.0 - fit_score) * 0.35))
+
+            result: Dict[str, Any] = {
+                "fitness": fit,
+                "rmse": rmse,
+                "max_corr": float(max_corr),
+                "accepted": acc,
+                "transform": np.array(reg_tf, dtype=np.float64),
+                "source": src_tag,
+                "src_points": int(len(src_d)),
+                "ref_points": int(len(tgt_d)),
+                "ref_points_full": int(len(ref_np_full)),
+                "tracking_window_used": bool(tracking_window_used),
+                "tracking_window_reason": str(tracking_window_reason),
+                "recovery_used": bool(used_recovery),
+                "temporal_guard_reject": bool(temporal_guard_reject),
+                "yaw_hypotheses": int(yaw_hypotheses),
+                "confidence": float(confidence),
+                "covariance_diag": [sigma_xy, sigma_xy, sigma_z, sigma_yaw, sigma_yaw, sigma_yaw],
+                "live_only": bool(use_live_src),
+                "global_reloc_allowed": bool(allow_global_reloc),
+                "global_reloc_blocked_by_guess": bool(global_reloc_blocked_by_guess),
+                "bidir_ok": bool(bidir.get("ok", False)),
+                "inlier_live_to_ref": float(bidir.get("live_to_ref_inlier", 0.0)),
+                "inlier_ref_to_live": float(bidir.get("ref_to_live_inlier", 0.0)),
+                "inlier_bidir": float(bidir.get("bidir_inlier", 0.0)),
+                "bidir_rmse": float(bidir.get("bidir_rmse", 9e9)),
+                "approx_guess_used": bool(approx_guess_used),
+                "approx_guess_fail_count": int(approx_pose_guess_fail_count),
+                "approx_guess_success_count": int(approx_pose_guess_success_count),
+                "approx_guess_offset_m": float(guess_offset_m),
+                "bootstrap_pending": bool(bootstrap_pending),
+                "place_retrieval_ok": bool(place_query.get("ok", False)),
+                "place_retrieval_reason": str(place_query.get("reason", "na")),
+                "place_retrieval_backend": str(place_query.get("backend", place_recog.backend_active)),
+                "place_retrieval_top_score": float(place_query.get("top_score", 0.0)),
+                "place_retrieval_candidates": int(len(place_query.get("candidates", []))),
+            }
+
+            last_localize_t = now
+            if not use_live_src:
+                last_localize_points = cur_n
+            if result["accepted"]:
+                prev_tf = np.array(slam_to_ref_transform, dtype=np.float64, copy=True) if slam_to_ref_valid else None
+                loc_frames.set_reference_alignment(reg_tf, confidence)
+                _sync_loc_vars_from_service()
+                if use_live_src and prev_tf is not None and submap_mode_enabled and submap_mapper.has_points:
+                    try:
+                        delta_tf = np.asarray(slam_to_ref_transform, dtype=np.float64) @ np.linalg.inv(prev_tf)
+                        dt_m, dr_deg = _tf_delta(np.eye(4, dtype=np.float64), delta_tf)
+                        if dt_m > 1.20 or dr_deg > 45.0:
+                            # Large relocalization jump: reset local/global buffer to avoid ghost structures.
+                            submap_mapper.reset()
+                        elif dt_m > 1e-4 or dr_deg > 1e-3:
+                            submap_mapper.apply_correction(delta_tf)
+                    except Exception as _sub_exc:
+                        st("WARN", f"[submap] correction after relocalization failed: {_sub_exc}")
+                approx_pose_guess_tf = None
+                approx_pose_guess_until_t = 0.0
+                approx_pose_guess_fail_count = 0
+                approx_pose_guess_success_count = 0
+                session_store.record_success(
+                    ref_map_path,
+                    last_localize_transform,
+                    float(result["fitness"]),
+                    float(result["rmse"]),
+                )
+            else:
+                if approx_guess_active and (not slam_to_ref_valid):
+                    if not bootstrap_pending:
+                        approx_pose_guess_fail_count += 1
+                loc_frames.update_confidence(max(0.0, min(1.0, loc_frames.confidence * 0.85)))
+                _sync_loc_vars_from_service()
+            safety.mark_localization(bool(result["accepted"]), now)
+            loc_state.update(result, now)
+            result["state"] = str(loc_state.state)
+            st("INFO", {"LOCALIZE": result})
+            push_loc_state()
+            return result
+
+        except Exception as e:
+            safety.mark_localization(False, now)
+            loc_state.update(None, now)
+            push_loc_state()
+            if diagnostics is not None:
+                diagnostics.log_exception("LOCALIZE failed", e, _worker_diag_state())
+            st("ERROR", f"LOCALIZE failed: {e}")
+            return None
+
+    def maybe_auto_localize(src_live: Optional[np.ndarray] = None):
+        if not loc_cfg.enabled:
+            return
+        if not ref_map_path:
+            return
+        if src_live is not None and localize_only_enabled:
+            do_localize(force=False, source="LIVE_ONLY", src_override=src_live)
+        else:
+            do_localize(force=False, source="AUTO")
+        t = _now()
+        if loc_state.should_recover(t):
+            loc_state.enter_recovery(t)
+            push_loc_state()
+            do_localize(force=True, source="RECOVERY", src_override=src_live if localize_only_enabled else None)
+
+    def apply_density_mode(mode: str):
+        nonlocal density_mode, live_stride
+        m = str(mode).upper().strip()
+        if m == "LOW":
+            density_mode = "LOW"
+            live_stride = max(2, base_stride * 4)
+        elif m == "HIGH":
+            density_mode = "HIGH"
+            live_stride = 1
+        else:
+            density_mode = "MEDIUM"
+            live_stride = base_stride
+        st("INFO", {"DENSITY": {"mode": density_mode, "stride": int(live_stride)}})
+
+    def _rebuild_filter_runtime(keep_existing: bool = True) -> None:
+        nonlocal filt
+        seed_pts = None
+        if keep_existing and stable is not None:
+            try:
+                seed_pts = stable.get_points()
+            except Exception:
+                seed_pts = None
+
+        vf_cfg = VFilterCfg(
+            voxel_size=float(runtime_filter_voxel),
+            hit_threshold=int(runtime_filter_hit_threshold),
+            decay_seconds=float(runtime_filter_decay),
+            max_voxels=int(filt_cfg.max_voxels),
+        )
+        new_f = VoxelPersistenceFilter(vf_cfg)
+        if seed_pts is not None and len(seed_pts) > 0 and hasattr(new_f, "seed_stable_points"):
+            try:
+                new_f.seed_stable_points(
+                    seed_pts,
+                    now=_now(),
+                    hit_count=int(runtime_filter_hit_threshold),
+                    clear_existing=True,
+                )
+            except Exception:
+                pass
+        filt = new_f
+
+    def apply_filter_tuning(payload: Any) -> None:
+        nonlocal runtime_filter_voxel, runtime_filter_hit_threshold, runtime_filter_decay
+        if not isinstance(payload, dict):
+            st("WARN", "SET_FILTER_TUNING ignored: payload must be dict.")
+            return
+        try:
+            runtime_filter_voxel = max(0.02, float(payload.get("voxel_size", runtime_filter_voxel)))
+        except Exception:
+            pass
+        try:
+            runtime_filter_hit_threshold = max(1, int(payload.get("hit_threshold", runtime_filter_hit_threshold)))
+        except Exception:
+            pass
+        try:
+            runtime_filter_decay = max(0.5, float(payload.get("decay_seconds", runtime_filter_decay)))
+        except Exception:
+            pass
+
+        _rebuild_filter_runtime(keep_existing=True)
+        st(
+            "INFO",
+            {
+                "FILTER_TUNING": {
+                    "voxel_size": float(runtime_filter_voxel),
+                    "hit_threshold": int(runtime_filter_hit_threshold),
+                    "decay_seconds": float(runtime_filter_decay),
+                    "profile": str(stability_profile),
+                }
+            },
+        )
+
+    def apply_stability_profile(profile_name: Any) -> None:
+        nonlocal stability_profile
+        p = str(profile_name or "BALANCED").upper().strip()
+        presets = {
+            # Keep map history much longer during exploration so points do not fade while moving away.
+            "MAP_NEW": {"hit_threshold": 2, "decay_seconds": 1800.0, "voxel_size": 0.10, "recommended_density": "MEDIUM"},
+            "EXTEND_MAP": {"hit_threshold": 2, "decay_seconds": 1800.0, "voxel_size": 0.10, "recommended_density": "MEDIUM"},
+            "LOCALIZE_MAP": {"hit_threshold": 3, "decay_seconds": 45.0, "voxel_size": 0.20, "recommended_density": "MEDIUM"},
+            "LIVE_NAV_MAP": {"hit_threshold": 2, "decay_seconds": 18.0, "voxel_size": 0.20, "recommended_density": "MEDIUM"},
+            "LIVE_NAV_NO_MAP": {"hit_threshold": 2, "decay_seconds": 14.0, "voxel_size": 0.18, "recommended_density": "HIGH"},
+            "QUICK_DEMO": {"hit_threshold": 2, "decay_seconds": 8.0, "voxel_size": 0.20, "recommended_density": "HIGH"},
+            "BALANCED": {
+                "voxel_size": float(max(0.02, filt_cfg.voxel_size)),
+                "hit_threshold": int(max(1, filt_cfg.hits_threshold)),
+                "decay_seconds": float(max(0.5, filt_cfg.strict_sec if filt_cfg.use_strict else filt_cfg.window_sec)),
+                "recommended_density": "MEDIUM",
+            },
+        }
+        if p not in presets:
+            st("WARN", f"Unknown stability profile: {p}.")
+            return
+        stability_profile = p
+        preset = presets[p]
+        apply_filter_tuning(
+            {
+                "voxel_size": float(preset.get("voxel_size", runtime_filter_voxel)),
+                "hit_threshold": int(preset["hit_threshold"]),
+                "decay_seconds": float(preset["decay_seconds"]),
+            }
+        )
+        apply_density_mode(str(preset.get("recommended_density", density_mode)))
+        st(
+            "INFO",
+            {
+                "WORKFLOW_PROFILE": {
+                    "name": str(stability_profile),
+                    "recommended_density": str(preset.get("recommended_density", density_mode)),
+                }
+            },
+        )
+
+    def _submap_profile_allowed() -> bool:
+        profile = str(stability_profile).upper().strip()
+        allowed = {str(x).upper().strip() for x in tuple(submap_cfg.apply_profiles)}
+        return profile in allowed
+
+    def apply_min_stable_points(value: Any) -> None:
+        try:
+            v = int(value)
+        except Exception:
+            st("WARN", f"SET_MIN_STABLE_POINTS ignored: invalid value {value!r}")
+            return
+        v = int(np.clip(v, 50, 5_000_000))
+        map_cfg.min_points_to_save = v
+        loc_cfg.min_points_for_localize = v
+        nav_cfg.min_points = v
+        try:
+            nav_exporter.update_config(nav_cfg)
+        except Exception:
+            pass
+        st("INFO", {"MIN_STABLE_POINTS": {"value": int(v)}})
+
+    def apply_loop_closure_mode(enabled: Any) -> None:
+        e = _as_bool(enabled, False)
+        loop_cfg.enabled = bool(e)
+        loop_backend.cfg.enabled = bool(e)
+        st("INFO", {"LOOP_MODE": {"enabled": bool(e)}})
+
+    def apply_loc_state_machine_mode(enabled: Any) -> None:
+        e = _as_bool(enabled, True)
+        loc_state_cfg.enabled = bool(e)
+        loc_state.set_enabled(bool(e))
+        st("INFO", {"LOC_MACHINE": {"enabled": bool(e)}})
+        push_loc_state()
+
+    def apply_submap_mode(payload: Any) -> None:
+        nonlocal submap_cfg, submap_mapper, submap_mode_enabled
+        merged = {
+            "enabled": bool(submap_mode_enabled),
+            "local_window_frames": int(submap_cfg.local_window_frames),
+            "local_voxel_m": float(submap_cfg.local_voxel_m),
+            "global_voxel_m": float(submap_cfg.global_voxel_m),
+            "merge_period_sec": float(submap_cfg.merge_period_sec),
+            "merge_min_translation_m": float(submap_cfg.merge_min_translation_m),
+            "merge_min_rotation_deg": float(submap_cfg.merge_min_rotation_deg),
+            "max_global_points": int(submap_cfg.max_global_points),
+            "display_max_points": int(submap_cfg.display_max_points),
+            "apply_profiles": list(submap_cfg.apply_profiles),
+        }
+        if isinstance(payload, dict):
+            merged.update(payload)
+        elif payload is not None:
+            merged["enabled"] = _as_bool(payload, bool(submap_mode_enabled))
+        new_cfg = SubmapConfig.from_dict(merged)
+        keep_pts = True
+        if isinstance(payload, dict):
+            keep_pts = _as_bool(payload.get("keep_points", True), True)
+        submap_cfg = new_cfg
+        submap_mode_enabled = bool(new_cfg.enabled)
+        submap_mapper.set_config(new_cfg, keep_points=bool(keep_pts and submap_mode_enabled))
+        if not submap_mode_enabled:
+            submap_mapper.reset()
+        st(
+            "INFO",
+            {
+                "SUBMAP_MODE": {
+                    "enabled": bool(submap_mode_enabled),
+                    "profile_allowed": bool(_submap_profile_allowed()),
+                    "apply_profiles": list(submap_cfg.apply_profiles),
+                    "local_window_frames": int(submap_cfg.local_window_frames),
+                    "local_voxel_m": float(submap_cfg.local_voxel_m),
+                    "global_voxel_m": float(submap_cfg.global_voxel_m),
+                }
+            },
+        )
+
+    def apply_approx_pose(payload: Any) -> None:
+        nonlocal approx_pose_guess_tf, approx_pose_guess_until_t
+        nonlocal approx_pose_guess_fail_count, approx_pose_guess_success_count
+        nonlocal last_localize_transform
+        if payload is None:
+            approx_pose_guess_tf = None
+            approx_pose_guess_until_t = 0.0
+            approx_pose_guess_fail_count = 0
+            approx_pose_guess_success_count = 0
+            st("INFO", {"APPROX_POSE": {"set": False}})
+            return
+        if not isinstance(payload, dict):
+            st("WARN", "SET_APPROX_POSE ignored: payload must be dict.")
+            return
+        try:
+            x = float(payload.get("x"))
+            y = float(payload.get("y"))
+        except Exception:
+            st("WARN", "SET_APPROX_POSE ignored: x,y are required.")
+            return
+        z = float(payload.get("z", loc_guess_default_z_m))
+        yaw_deg = payload.get("yaw_deg", None)
+        if yaw_deg is None:
+            yaw_deg = _yaw_deg_from_tf(last_localize_transform)
+        else:
+            yaw_deg = float(yaw_deg)
+        tf = _tf_from_xyzyaw(x, y, z, yaw_deg)
+        approx_pose_guess_tf = np.asarray(tf, dtype=np.float64)
+        approx_pose_guess_until_t = float(_now() + float(loc_guess_ttl_sec))
+        approx_pose_guess_fail_count = 0
+        approx_pose_guess_success_count = 0
+        st(
+            "INFO",
+            {
+                "APPROX_POSE": {
+                    "set": True,
+                    "x": float(x),
+                    "y": float(y),
+                    "z": float(z),
+                    "yaw_deg": float(yaw_deg),
+                    "ttl_sec": float(loc_guess_ttl_sec),
+                }
+            },
+        )
+
+    def apply_autosave(payload: Any) -> None:
+        nonlocal autosave_enabled, autosave_interval_sec, autosave_base_name
+        if not isinstance(payload, dict):
+            st("WARN", "SET_AUTOSAVE ignored: payload must be dict.")
+            return
+        autosave_enabled = _as_bool(payload.get("enabled", autosave_enabled), autosave_enabled)
+        try:
+            autosave_interval_sec = max(5.0, float(payload.get("interval_sec", autosave_interval_sec)))
+        except Exception:
+            pass
+        base_in = str(payload.get("base_name", autosave_base_name)).strip()
+        if base_in:
+            autosave_base_name = base_in
+        st(
+            "INFO",
+            {
+                "AUTOSAVE": {
+                    "enabled": bool(autosave_enabled),
+                    "interval_sec": float(autosave_interval_sec),
+                    "base_name": str(autosave_base_name),
+                }
+            },
+        )
+
+    def apply_nav_config(payload: Any) -> None:
+        nonlocal nav_cfg, nav_rt_cfg, nav_runtime, nav_global_planner
+        if not isinstance(payload, dict):
+            st("WARN", "SET_NAV_CONFIG ignored: payload must be dict.")
+            return
+        candidate = nav_cfg.patched(payload)
+        if float(candidate.z_max_m) <= float(candidate.z_min_m):
+            st("WARN", "SET_NAV_CONFIG ignored: z_max_m must be greater than z_min_m.")
+            return
+        nav_cfg = candidate
+        nav_exporter.update_config(nav_cfg)
+        nav_rt_cfg = nav_rt_cfg.patched(
+            {
+                "resolution_m": float(nav_cfg.resolution_m),
+                "z_min_m": float(nav_cfg.z_min_m),
+                "z_max_m": float(nav_cfg.z_max_m),
+                "padding_m": float(nav_cfg.padding_m),
+                "inflation_radius_m": float(nav_cfg.inflation_radius_m),
+            }
+        )
+        nav_runtime = LiveCostmapRuntime(nav_rt_cfg)
+        nav_global_planner = GlobalAStarPlanner(blocked_cost=int(nav_rt_cfg.blocked_cost))
+        st(
+            "INFO",
+            {
+                "NAV_CONFIG": {
+                    "min_points": int(nav_cfg.min_points),
+                    "resolution_m": float(nav_cfg.resolution_m),
+                    "z_min_m": float(nav_cfg.z_min_m),
+                    "z_max_m": float(nav_cfg.z_max_m),
+                    "inflation_radius_m": float(nav_cfg.inflation_radius_m),
+                    "padding_m": float(nav_cfg.padding_m),
+                }
+            },
+        )
+
+    def apply_map_quality_config(payload: Any) -> None:
+        nonlocal map_quality_cfg, map_quality
+        if not isinstance(payload, dict):
+            st("WARN", "SET_MAP_QUALITY ignored: payload must be dict.")
+            return
+        merged = {
+            "enabled": map_quality_cfg.enabled,
+            "near_min_range_m": map_quality_cfg.near_min_range_m,
+            "ray_consistency_enabled": map_quality_cfg.ray_consistency_enabled,
+            "ray_bin_deg": map_quality_cfg.ray_bin_deg,
+            "ray_elev_bin_deg": map_quality_cfg.ray_elev_bin_deg,
+            "ray_max_behind_m": map_quality_cfg.ray_max_behind_m,
+            "ray_keep_n": map_quality_cfg.ray_keep_n,
+            "world_z_clip_enabled": map_quality_cfg.world_z_clip_enabled,
+            "world_z_min_m": map_quality_cfg.world_z_min_m,
+            "world_z_max_m": map_quality_cfg.world_z_max_m,
+            "outlier_filter_enabled": map_quality_cfg.outlier_filter_enabled,
+            "outlier_voxel_m": map_quality_cfg.outlier_voxel_m,
+            "outlier_min_points": map_quality_cfg.outlier_min_points,
+        }
+        merged.update(payload)
+        map_quality_cfg = IndoorMapQualityConfig.from_dict(merged)
+        map_quality = IndoorMapQualityFilter(map_quality_cfg)
+        st(
+            "INFO",
+            {
+                "MAP_QUALITY": {
+                    "enabled": bool(map_quality_cfg.enabled),
+                    "near_min_range_m": float(map_quality_cfg.near_min_range_m),
+                    "ray_consistency_enabled": bool(map_quality_cfg.ray_consistency_enabled),
+                    "ray_bin_deg": float(map_quality_cfg.ray_bin_deg),
+                    "ray_elev_bin_deg": float(map_quality_cfg.ray_elev_bin_deg),
+                    "ray_max_behind_m": float(map_quality_cfg.ray_max_behind_m),
+                    "ray_keep_n": int(map_quality_cfg.ray_keep_n),
+                    "world_z_clip_enabled": bool(map_quality_cfg.world_z_clip_enabled),
+                    "world_z_min_m": float(map_quality_cfg.world_z_min_m),
+                    "world_z_max_m": float(map_quality_cfg.world_z_max_m),
+                    "outlier_filter_enabled": bool(map_quality_cfg.outlier_filter_enabled),
+                    "outlier_voxel_m": float(map_quality_cfg.outlier_voxel_m),
+                    "outlier_min_points": int(map_quality_cfg.outlier_min_points),
+                }
+            },
+        )
+
+    def apply_nav_runtime_config(payload: Any) -> None:
+        nonlocal nav_rt_cfg, nav_runtime, nav_global_planner, nav_path_world, nav_last_plan_t, nav_last_update_t
+        if not isinstance(payload, dict):
+            st("WARN", "SET_NAV_RUNTIME ignored: payload must be dict.")
+            return
+        nav_rt_cfg = nav_rt_cfg.patched(payload)
+        nav_runtime = LiveCostmapRuntime(nav_rt_cfg)
+        nav_global_planner = GlobalAStarPlanner(blocked_cost=int(nav_rt_cfg.blocked_cost))
+        nav_path_world = []
+        nav_last_plan_t = 0.0
+        nav_last_update_t = 0.0
+        st(
+            "INFO",
+            {
+                "NAV_RUNTIME": {
+                    "enabled": bool(nav_rt_cfg.enabled),
+                    "resolution_m": float(nav_rt_cfg.resolution_m),
+                    "z_min_m": float(nav_rt_cfg.z_min_m),
+                    "z_max_m": float(nav_rt_cfg.z_max_m),
+                    "inflation_radius_m": float(nav_rt_cfg.inflation_radius_m),
+                    "dynamic_decay_sec": float(nav_rt_cfg.dynamic_decay_sec),
+                    "dynamic_min_hits": int(nav_rt_cfg.dynamic_min_hits),
+                    "blocked_cost": int(nav_rt_cfg.blocked_cost),
+                }
+            },
+        )
+
+    def apply_nav_goal(payload: Any) -> None:
+        nonlocal nav_goal_xy, nav_path_world, nav_last_plan_t
+        if not isinstance(payload, dict):
+            st("WARN", "SET_NAV_GOAL ignored: payload must be dict.")
+            return
+        try:
+            x = float(payload.get("x"))
+            y = float(payload.get("y"))
+        except Exception:
+            st("WARN", "SET_NAV_GOAL ignored: x/y required.")
+            return
+        nav_goal_xy = (x, y)
+        nav_path_world = []
+        nav_last_plan_t = 0.0
+        st("INFO", {"NAV_GOAL": {"x": float(x), "y": float(y)}})
+
+    def clear_nav_goal() -> None:
+        nonlocal nav_goal_xy, nav_path_world
+        nav_goal_xy = None
+        nav_path_world = []
+        st("INFO", {"NAV_GOAL": None})
+
+    def mission_start(payload: Any) -> None:
+        nonlocal nav_goal_xy, nav_path_world, nav_last_plan_t
+        if not isinstance(payload, dict):
+            st("WARN", "MISSION_START ignored: payload must be dict.")
+            return
+        raw = payload.get("waypoints", [])
+        waypoints: List[Tuple[float, float]] = []
+        if isinstance(raw, list):
+            for wp in raw:
+                if isinstance(wp, dict):
+                    try:
+                        waypoints.append((float(wp.get("x")), float(wp.get("y"))))
+                    except Exception:
+                        continue
+                elif isinstance(wp, (list, tuple)) and len(wp) >= 2:
+                    try:
+                        waypoints.append((float(wp[0]), float(wp[1])))
+                    except Exception:
+                        continue
+        mission.start(waypoints=waypoints)
+        goal = mission.current_goal()
+        nav_goal_xy = None if goal is None else (float(goal[0]), float(goal[1]))
+        nav_path_world = []
+        nav_last_plan_t = 0.0
+        st("INFO", {"MISSION": mission.snapshot()})
+
+    def mission_pause() -> None:
+        mission.pause()
+        st("INFO", {"MISSION": mission.snapshot()})
+
+    def mission_resume() -> None:
+        mission.resume()
+        st("INFO", {"MISSION": mission.snapshot()})
+
+    def mission_stop() -> None:
+        nonlocal nav_goal_xy, nav_path_world
+        mission.stop()
+        nav_goal_xy = None
+        nav_path_world = []
+        st("INFO", {"MISSION": mission.snapshot()})
+
+    def _safe_record_base(name: str) -> str:
+        base = (name or "").strip() or "slam_recording"
+        for bad in ("/", "\\", ":", "*", "?", "\"", "<", ">", "|"):
+            base = base.replace(bad, "_")
+        return base
+
+    def record_start(payload: Any) -> None:
+        nonlocal recording_enabled, recording_frames, recording_poses, recording_base_name, recording_drop_count
+        base = recording_base_name
+        if isinstance(payload, dict):
+            b = str(payload.get("base_name", "")).strip()
+            if b:
+                base = b
+        recording_base_name = _safe_record_base(base)
+        recording_frames = []
+        recording_poses = []
+        recording_drop_count = 0
+        recording_enabled = True
+        st(
+            "INFO",
+            {
+                "RECORDING": {
+                    "enabled": True,
+                    "base_name": str(recording_base_name),
+                    "max_frames": int(recording_max_frames),
+                }
+            },
+        )
+
+    def record_stop(payload: Any) -> None:
+        nonlocal recording_enabled, recording_frames, recording_poses, recording_base_name
+        save = True
+        base = recording_base_name
+        if isinstance(payload, dict):
+            save = _as_bool(payload.get("save", True), True)
+            b = str(payload.get("base_name", "")).strip()
+            if b:
+                base = b
+        recording_enabled = False
+        if (not save) or len(recording_frames) == 0:
+            st(
+                "INFO",
+                {
+                    "RECORDING": {
+                        "enabled": False,
+                        "frames": int(len(recording_frames)),
+                        "saved": False,
+                    }
+                },
+            )
+            recording_frames = []
+            recording_poses = []
+            return
+        base = _safe_record_base(base)
+        stamp = time.strftime("%Y%m%d_%H%M%S", time.localtime())
+        out_path = Path(map_cfg.data_folder) / f"{base}_{stamp}.npz"
+        try:
+            frames_obj = np.empty((len(recording_frames),), dtype=object)
+            for i, fr in enumerate(recording_frames):
+                frames_obj[i] = np.asarray(fr, dtype=np.float32)
+            if len(recording_poses) == len(recording_frames) and len(recording_poses) > 0:
+                poses_np = np.stack(recording_poses, axis=0).astype(np.float32)
+                np.savez_compressed(out_path, frames=frames_obj, poses=poses_np)
+            else:
+                np.savez_compressed(out_path, frames=frames_obj)
+            st(
+                "INFO",
+                {
+                    "RECORD_SAVED": {
+                        "path": str(out_path),
+                        "frames": int(len(recording_frames)),
+                        "dropped": int(recording_drop_count),
+                    }
+                },
+            )
+        except Exception as e:
+            st("ERROR", f"RECORD save failed: {e}")
+        recording_frames = []
+        recording_poses = []
+
+    def _queue_lag() -> int:
+        try:
+            return int(data_q.qsize())
+        except Exception:
+            return -1
+
+    def _update_perf_rollup(now_t: float, stable_count: int) -> None:
+        nonlocal perf_input_count, perf_publish_count, perf_last_rate_t
+        nonlocal perf_last_cpu_wall_t, perf_last_cpu_proc_t
+        nonlocal perf_input_fps, perf_publish_fps, perf_cpu_pct
+        nonlocal perf_queue_lag, perf_stable_growth, perf_prev_stable_count, perf_prev_stable_t
+
+        dt_rates = float(now_t - perf_last_rate_t)
+        if dt_rates >= 0.8:
+            perf_input_fps = float(perf_input_count) / max(dt_rates, 1e-6)
+            perf_publish_fps = float(perf_publish_count) / max(dt_rates, 1e-6)
+            perf_input_count = 0
+            perf_publish_count = 0
+            perf_last_rate_t = now_t
+
+        dt_cpu = float(now_t - perf_last_cpu_wall_t)
+        if dt_cpu >= 0.8:
+            cpu_now = float(time.process_time())
+            perf_cpu_pct = 100.0 * float(cpu_now - perf_last_cpu_proc_t) / max(dt_cpu, 1e-6)
+            perf_last_cpu_proc_t = cpu_now
+            perf_last_cpu_wall_t = now_t
+
+        dt_growth = float(now_t - perf_prev_stable_t)
+        if dt_growth >= 0.8:
+            perf_stable_growth = float(stable_count - perf_prev_stable_count) / max(dt_growth, 1e-6)
+            perf_prev_stable_count = int(stable_count)
+            perf_prev_stable_t = now_t
+
+        perf_queue_lag = _queue_lag()
+
+    def maybe_autosave(now_t: float) -> None:
+        nonlocal last_autosave_t
+        if not autosave_enabled or not mapping_enabled:
+            return
+        if (now_t - float(last_autosave_t)) < float(autosave_interval_sec):
+            return
+        try:
+            if cleanup_enabled:
+                full_pts = stable.get_points()
+                if full_pts is not None and len(full_pts) >= int(cleanup_min_total_points):
+                    cleaned, _ = cleanup_map_islands(full_pts)
+                    if cleaned is not None and len(cleaned) >= int(map_cfg.min_points_to_save):
+                        stable.set_points(cleaned)
+            pts = stable.get_save_points()
+            n = 0 if pts is None else int(len(pts))
+            req = int(map_cfg.min_points_to_save)
+            if n < req:
+                last_autosave_t = now_t
+                return
+            out_path = stable.export_map(str(autosave_base_name))
+            last_autosave_t = now_t
+            st("INFO", {"AUTOSAVED": {"path": str(out_path), "points": int(n)}})
+        except Exception as e:
+            last_autosave_t = now_t
+            st("WARN", f"AUTOSAVE failed: {e}")
+
+        # Checkpoint the submap alongside autosave
+        if submap_ckpt is not None and submap_mode_enabled:
+            submap_ckpt.save(submap_mapper)
+
+    # ---------------- lidar frame processor ----------------
+    def process_points(points: np.ndarray):
+        nonlocal last_pub, mapping_enabled, localize_only_enabled, worker_mode, last_key_pose, last_frame_t
+        nonlocal last_guard_pose, last_guard_t, guard_reject_count, guard_last_warn_t
+        nonlocal odom_to_map_transform, map_lock_last_t, map_lock_accept_count, map_lock_reject_count, map_lock_last_warn_t
+        nonlocal continuity_prev_pts, continuity_reject_count, continuity_consecutive_rejects, continuity_last_warn_t
+        nonlocal rotate_freeze_count, rotate_last_warn_t
+        nonlocal cleanup_last_t, cleanup_removed_total, cleanup_last_info
+        nonlocal slam_to_ref_transform, slam_to_ref_valid, last_localize_transform
+        nonlocal perf_input_count, perf_publish_count, perf_icp_ms
+        nonlocal recording_enabled, recording_frames, recording_poses, recording_drop_count
+        nonlocal nav_goal_xy, nav_last_update_t, nav_last_plan_t, nav_path_world
+        nonlocal latest_live_pts, latest_live_localize_pts
+        nonlocal ref_points_cache, ref_nav_points_cache, ref_nav_cache_src_n
+
+        if points is None or len(points) == 0:
+            return
+
+        perf_input_count += 1
+
+        pts = points.astype(np.float32, copy=True)
+        pts[:, 1] *= -1
+        pts[:, 2] *= -1
+
+        # Reject invalid values and out-of-range returns before SLAM.
+        finite_mask = np.isfinite(pts).all(axis=1)
+        if not np.all(finite_mask):
+            pts = pts[finite_mask]
+        if len(pts) == 0:
+            return
+
+        r2 = np.einsum("ij,ij->i", pts, pts)
+        min_r = float(map_quality_cfg.near_min_range_m if map_quality_cfg.enabled else 0.15)
+        min_r2 = min_r * min_r
+        max_r = float(eng_cfg.max_range)
+        max_r2 = max_r * max_r if max_r > 0 else np.inf
+        range_mask = (r2 >= min_r2) & (r2 <= max_r2)
+        if not np.all(range_mask):
+            pts = pts[range_mask]
+        if len(pts) < 40:
+            return
+
+        # pre-downsample stride from config
+        stride = int(live_stride)
+        if stride > 1:
+            pts = pts[::stride]
+        if map_quality_cfg.enabled:
+            pts = map_quality.apply_sensor(pts)
+            if len(pts) < 40:
+                return
+        latest_live_pts = np.array(pts, dtype=np.float32, copy=True)
+        latest_live_localize_pts = None
+
+        pose = None
+        stable_pts_display = None
+        stable_cols = None
+        fusion_info: Dict[str, Any] = {}
+        nav_runtime_info = None
+        nav_info: Dict[str, Any] = {}
+        safety_info: Dict[str, Any] = {}
+        map_lock_info: Dict[str, Any] = {"status": "skip", "ran": False}
+        continuity_info: Dict[str, Any] = {"status": "na"}
+        cleanup_info: Dict[str, Any] = dict(cleanup_last_info) if isinstance(cleanup_last_info, dict) else {"status": "na"}
+        rotate_freeze_active = False
+        submap_info: Dict[str, Any] = submap_mapper.status(
+            active=False,
+            reason="idle",
+            profile=str(stability_profile),
+            profile_allowed=_submap_profile_allowed(),
+        )
+        ref_match_points_pub = None
+        ref_match_colors_pub = None
+        ref_match_stats: Dict[str, Any] = {}
+        stable_points_in_global_frame = False
+        mode_now = str(worker_mode)
+
+        if mapping_enabled:
+            now_t = _now()
+            dt = 0.1 if last_frame_t <= 0 else float(np.clip(now_t - last_frame_t, 0.02, 0.25))
+            last_frame_t = now_t
+            ts = np.linspace(0.0, dt, len(pts), dtype=np.float64)
+
+            t_icp0 = _now()
+            slam.register_frame(pts, ts)
+            perf_icp_ms = 1000.0 * (_now() - t_icp0)
+            odom_pose = slam.last_pose if hasattr(slam, "last_pose") else getattr(slam, "pose", None)
+            if odom_pose is not None:
+                odom_pose, fusion_info = sensor_fusion.fuse_pose(np.asarray(odom_pose, dtype=np.float64), now=now_t)
+                try:
+                    pose = np.asarray(odom_to_map_transform, dtype=np.float64) @ np.asarray(odom_pose, dtype=np.float64)
+                except Exception:
+                    pose = np.asarray(odom_pose, dtype=np.float64)
+
+            # Reject obviously unstable jumps to avoid "wall on wall" artifacts.
+            unstable_jump = False
+            trans_step_for_mode = 0.0
+            rot_step_for_mode = 0.0
+            rotating_in_place = False
+            if pose_guard_enabled and pose is not None:
+                pose_np = np.asarray(pose, dtype=np.float64)
+                if last_guard_pose is not None:
+                    dpos = pose_np[:3, 3] - last_guard_pose[:3, 3]
+                    trans_step = float(np.linalg.norm(dpos))
+                    rrel = pose_np[:3, :3] @ last_guard_pose[:3, :3].T
+                    rot_step = float(np.degrees(np.arccos(np.clip((np.trace(rrel) - 1.0) * 0.5, -1.0, 1.0))))
+                    trans_step_for_mode = float(trans_step)
+                    rot_step_for_mode = float(rot_step)
+                    if rotate_mode_enabled and trans_step_for_mode <= float(rotate_in_place_max_trans_m) and rot_step_for_mode >= float(rotate_in_place_min_rot_deg):
+                        rotating_in_place = True
+                    scale = max(1.0, float(dt) / max(1e-3, float(pose_guard_ref_dt)))
+                    max_t = float(pose_guard_max_trans) * scale
+                    max_r = float(pose_guard_max_rot_deg) * scale
+                    if rotating_in_place:
+                        max_r = max(max_r, float(rotate_pose_guard_max_rot_deg) * scale)
+                    if trans_step > max_t or rot_step > max_r:
+                        unstable_jump = True
+                        guard_reject_count += 1
+                        pose = np.array(last_guard_pose, dtype=np.float64, copy=True)
+                        stable_pts_display = stable.get_display_points()
+                        if (now_t - guard_last_warn_t) > 1.0:
+                            guard_last_warn_t = now_t
+                            st(
+                                "WARN",
+                                (
+                                    "MOTION_GUARD rejected frame: "
+                                    f"dpos={trans_step:.2f}m>{max_t:.2f}m or drot={rot_step:.1f}deg>{max_r:.1f}deg "
+                                    f"(count={guard_reject_count})"
+                                ),
+                            )
+                    else:
+                        last_guard_pose = np.array(pose_np, dtype=np.float64, copy=True)
+                        last_guard_t = now_t
+                else:
+                    last_guard_pose = np.array(pose_np, dtype=np.float64, copy=True)
+                    last_guard_t = now_t
+            elif pose is not None and rotate_mode_enabled and last_key_pose is not None:
+                # Fallback rotate-in-place detector when pose-guard is disabled.
+                try:
+                    dpos = np.asarray(pose, dtype=np.float64)[:3, 3] - np.asarray(last_key_pose, dtype=np.float64)[:3, 3]
+                    trans_step_for_mode = float(np.linalg.norm(dpos))
+                    rrel = np.asarray(pose, dtype=np.float64)[:3, :3] @ np.asarray(last_key_pose, dtype=np.float64)[:3, :3].T
+                    rot_step_for_mode = float(np.degrees(np.arccos(np.clip((np.trace(rrel) - 1.0) * 0.5, -1.0, 1.0))))
+                    rotating_in_place = bool(
+                        trans_step_for_mode <= float(rotate_in_place_max_trans_m)
+                        and rot_step_for_mode >= float(rotate_in_place_min_rot_deg)
+                    )
+                except Exception:
+                    rotating_in_place = False
+
+            if not unstable_jump:
+                map_lock_block_frame = False
+                # Periodic scan-to-map correction keeps the accumulated map anchored
+                # and reduces duplicated structures when traversing long paths.
+                if pose is not None:
+                    ml_overrides = None
+                    if rotating_in_place and rotate_mode_enabled:
+                        ml_overrides = {
+                            "period_sec": float(rotate_map_lock_period_sec),
+                            "accept_fitness": float(rotate_map_lock_accept_fitness),
+                            "accept_rmse": float(rotate_map_lock_accept_rmse),
+                            "max_step_rotation_deg": float(rotate_map_lock_max_step_rot_deg),
+                        }
+                    corr_lock, map_lock_info = compute_map_lock_correction(pts, pose, now_t, overrides=ml_overrides)
+                    if corr_lock is not None:
+                        corr_np = np.asarray(corr_lock, dtype=np.float64)
+                        corr_apply = np.asarray(corr_np, dtype=np.float64, copy=True)
+                        if map_lock_apply_damping:
+                            alpha_t = float(map_lock_apply_alpha_t)
+                            alpha_r = float(map_lock_apply_alpha_r)
+                            max_t_apply = float(map_lock_apply_max_trans_m)
+                            max_r_apply = float(map_lock_apply_max_rot_deg)
+                            if rotating_in_place and rotate_mode_enabled:
+                                alpha_t = float(rotate_map_lock_apply_alpha_t)
+                                alpha_r = float(rotate_map_lock_apply_alpha_r)
+                                max_t_apply = float(rotate_map_lock_apply_max_trans_m)
+                                max_r_apply = float(rotate_map_lock_apply_max_rot_deg)
+                            corr_apply = _blend_rigid_tf(
+                                np.eye(4, dtype=np.float64),
+                                corr_apply,
+                                alpha_t=float(alpha_t),
+                                alpha_r=float(alpha_r),
+                            )
+                            tvec = np.asarray(corr_apply[:3, 3], dtype=np.float64)
+                            tnorm = float(np.linalg.norm(tvec))
+                            if tnorm > float(max_t_apply) and tnorm > 1e-9:
+                                corr_apply[:3, 3] = tvec * (float(max_t_apply) / tnorm)
+                            dpos_apply, drot_apply = _tf_delta(np.eye(4, dtype=np.float64), corr_apply)
+                            if drot_apply > float(max_r_apply) and drot_apply > 1e-6:
+                                corr_apply = _blend_rigid_tf(
+                                    np.eye(4, dtype=np.float64),
+                                    corr_apply,
+                                    alpha_t=1.0,
+                                    alpha_r=float(max_r_apply) / float(drot_apply),
+                                )
+                        else:
+                            dpos_apply, drot_apply = _tf_delta(np.eye(4, dtype=np.float64), corr_apply)
+                        map_lock_info["applied_dpos_m"] = float(dpos_apply)
+                        map_lock_info["applied_drot_deg"] = float(drot_apply)
+                        map_lock_info["damped"] = bool(map_lock_apply_damping)
+                        pose = corr_apply @ np.asarray(pose, dtype=np.float64)
+                        loc_frames.apply_map_correction(corr_apply)
+                        _sync_loc_vars_from_service()
+                        if last_guard_pose is not None:
+                            last_guard_pose = corr_apply @ np.asarray(last_guard_pose, dtype=np.float64)
+                        if last_key_pose is not None:
+                            last_key_pose = corr_apply @ np.asarray(last_key_pose, dtype=np.float64)
+                        map_lock_accept_count += 1
+                    elif bool(map_lock_info.get("ran", False)):
+                        map_lock_reject_count += 1
+                        block_on_reject_now = bool(map_lock_block_on_reject)
+                        if rotating_in_place and rotate_mode_enabled:
+                            block_on_reject_now = bool(rotate_map_lock_block_on_reject)
+                        if block_on_reject_now:
+                            map_lock_block_frame = True
+                            # Retry map-lock quickly after a reject to recover alignment faster.
+                            map_lock_last_t = float(now_t - map_lock_period_sec + map_lock_retry_after_sec)
+                            if (now_t - map_lock_last_warn_t) > 1.0:
+                                map_lock_last_warn_t = now_t
+                                fit_r = float(map_lock_info.get("fitness", 0.0))
+                                rmse_r = float(map_lock_info.get("rmse", 9e9))
+                                dpos_r = float(map_lock_info.get("dpos_m", 0.0))
+                                drot_r = float(map_lock_info.get("drot_deg", 0.0))
+                                st(
+                                    "WARN",
+                                    (
+                                        "MAP_LOCK rejected; skipping frame integration: "
+                                        f"fit={fit_r:.2f}, rmse={rmse_r:.2f}, "
+                                        f"dpos={dpos_r:.2f}m, drot={drot_r:.1f}deg"
+                                    ),
+                                )
+                        block_on_severe_now = bool(map_lock_block_on_severe)
+                        if rotating_in_place and rotate_mode_enabled:
+                            block_on_severe_now = bool(rotate_map_lock_block_on_severe)
+                        if block_on_severe_now:
+                            fit_r = float(map_lock_info.get("fitness", 0.0))
+                            rmse_r = float(map_lock_info.get("rmse", 9e9))
+                            dpos_r = float(map_lock_info.get("dpos_m", 0.0))
+                            drot_r = float(map_lock_info.get("drot_deg", 0.0))
+                            severe_reject = bool(
+                                fit_r <= float(map_lock_severe_fit)
+                                or rmse_r >= float(map_lock_severe_rmse)
+                                or dpos_r >= float(map_lock_severe_trans_m)
+                                or drot_r >= float(map_lock_severe_rot_deg)
+                            )
+                            if severe_reject:
+                                map_lock_block_frame = True
+                                if (now_t - map_lock_last_warn_t) > 1.0:
+                                    map_lock_last_warn_t = now_t
+                                    st(
+                                        "WARN",
+                                        (
+                                            "MAP_LOCK severe reject; skipping frame integration: "
+                                            f"fit={fit_r:.2f}, rmse={rmse_r:.2f}, "
+                                            f"dpos={dpos_r:.2f}m, drot={drot_r:.1f}deg"
+                                        ),
+                                    )
+
+                # keyframe gating from config
+                if map_lock_block_frame:
+                    stable_pts_display = stable.get_display_points()
+                elif eng_cfg.keyframe_enabled and pose is not None and not pose_delta_ok(pose, last_key_pose):
+                    # skip filter/map update
+                    stable_pts_display = stable.get_display_points()
+                else:
+                    if pose is not None:
+                        last_key_pose = pose
+
+                    points_world = to_world_points(pts, pose)
+                    stable_now = stable.get_points()
+                    stable_now_n = 0 if stable_now is None else int(len(stable_now))
+                    rotate_freeze_active = bool(
+                        rotate_mode_enabled
+                        and rotate_freeze_mapping
+                        and rotating_in_place
+                        and stable_now_n >= int(rotate_freeze_min_stable_pts)
+                    )
+                    if rotate_freeze_active:
+                        rotate_freeze_count += 1
+                        continuity_consecutive_rejects = 0
+                        continuity_pts_ds = voxel_downsample_np(points_world, float(continuity_voxel_m))
+                        continuity_prev_pts = (
+                            np.array(continuity_pts_ds, dtype=np.float32, copy=True)
+                            if continuity_pts_ds is not None and len(continuity_pts_ds) > 0
+                            else continuity_prev_pts
+                        )
+                        continuity_info = {
+                            "status": "skip_rotate_freeze",
+                            "trans_step_m": float(trans_step_for_mode),
+                            "rot_step_deg": float(rot_step_for_mode),
+                            "stable_points": int(stable_now_n),
+                            "freeze_count": int(rotate_freeze_count),
+                        }
+                        stable_pts_display = stable.get_display_points()
+                        if (now_t - rotate_last_warn_t) > 1.0:
+                            rotate_last_warn_t = now_t
+                            st(
+                                "INFO",
+                                (
+                                    "ROTATE_FREEZE active; skipping map integration during in-place turn "
+                                    f"(stable={stable_now_n}, dpos={trans_step_for_mode:.3f}m, drot={rot_step_for_mode:.1f}deg)"
+                                ),
+                            )
+                    else:
+                        if rotating_in_place and rotate_mode_enabled and rotate_skip_continuity:
+                            continuity_ok = True
+                            continuity_pts_ds = voxel_downsample_np(points_world, float(continuity_voxel_m))
+                            continuity_info = {
+                                "status": "skip_rotating_in_place",
+                                "trans_step_m": float(trans_step_for_mode),
+                                "rot_step_deg": float(rot_step_for_mode),
+                            }
+                        else:
+                            continuity_ok, continuity_info, continuity_pts_ds = check_frame_continuity(points_world)
+                        if not continuity_ok:
+                            continuity_reject_count += 1
+                            continuity_consecutive_rejects += 1
+                            if continuity_consecutive_rejects >= int(continuity_recover_after_rejects):
+                                continuity_ok = True
+                                continuity_consecutive_rejects = 0
+                                continuity_info = {
+                                    "status": "resync_after_rejects",
+                                    "reject_streak": int(continuity_recover_after_rejects),
+                                    "trans_step_m": float(trans_step_for_mode),
+                                    "rot_step_deg": float(rot_step_for_mode),
+                                }
+                                continuity_prev_pts = (
+                                    np.array(continuity_pts_ds, dtype=np.float32, copy=True)
+                                    if continuity_pts_ds is not None and len(continuity_pts_ds) > 0
+                                    else continuity_prev_pts
+                                )
+                                st(
+                                    "WARN",
+                                    (
+                                        "CONTINUITY auto-resync after repeated rejects; "
+                                        f"recover_after={int(continuity_recover_after_rejects)}"
+                                    ),
+                                )
+                        if not continuity_ok:
+                            stable_pts_display = stable.get_display_points()
+                            # Trigger a quick map-lock retry after continuity rejection.
+                            map_lock_last_t = float(now_t - map_lock_period_sec + map_lock_retry_after_sec)
+                            if (now_t - continuity_last_warn_t) > 1.0:
+                                continuity_last_warn_t = now_t
+                                st(
+                                    "WARN",
+                                    (
+                                        "CONTINUITY rejected; skipping frame integration: "
+                                        f"inlier={float(continuity_info.get('inlier_ratio', 0.0)):.2f}, "
+                                        f"med={float(continuity_info.get('median_m', 0.0)):.2f}m, "
+                                        f"p90={float(continuity_info.get('p90_m', 0.0)):.2f}m "
+                                        f"(count={int(continuity_reject_count)})"
+                                    ),
+                                )
+                        else:
+                            continuity_consecutive_rejects = 0
+                            continuity_prev_pts = (
+                                np.array(continuity_pts_ds, dtype=np.float32, copy=True)
+                                if continuity_pts_ds is not None and len(continuity_pts_ds) > 0
+                                else None
+                            )
+                            if map_quality_cfg.enabled:
+                                points_world = map_quality.apply_world(points_world)
+                            filt.update(points_world)
+                            stable_pts = filt.get_stable_points()
+                            stable.set_points(stable_pts)
+                            if (
+                                cleanup_enabled
+                                and stable_pts is not None
+                                and len(stable_pts) >= int(cleanup_min_total_points)
+                                and (now_t - float(cleanup_last_t)) >= float(cleanup_period_sec)
+                            ):
+                                cleanup_last_t = float(now_t)
+                                cleaned_pts, cleanup_info = cleanup_map_islands(stable_pts)
+                                cleanup_last_info = dict(cleanup_info)
+                                removed_now = int(cleanup_info.get("removed", 0))
+                                if removed_now > 0:
+                                    cleanup_removed_total += int(removed_now)
+                                    stable.set_points(cleaned_pts)
+                                    if hasattr(filt, "seed_stable_points"):
+                                        try:
+                                            filt.seed_stable_points(
+                                                cleaned_pts,
+                                                now=now_t,
+                                                hit_count=int(runtime_filter_hit_threshold),
+                                                clear_existing=True,
+                                            )
+                                        except Exception as _cln_exc:
+                                            st("WARN", f"[cleanup] filter re-seed after cleanup failed: {_cln_exc}")
+                                    st(
+                                        "INFO",
+                                        {
+                                            "MAP_CLEANUP": {
+                                                **cleanup_info,
+                                                "removed_total": int(cleanup_removed_total),
+                                            }
+                                        },
+                                    )
+                            else:
+                                cleanup_info = dict(cleanup_last_info) if isinstance(cleanup_last_info, dict) else {"status": "na"}
+                            stable_pts_display = stable.get_display_points()
+
+                # Lightweight loop closure + pose-graph optimization.
+                if pose is not None and loop_cfg.enabled:
+                    loop_res = loop_backend.process_frame(pts, np.asarray(pose, dtype=np.float64))
+                    if loop_res.loop_detected or loop_res.optimized or ("rejected" in loop_res.info):
+                        st(
+                            "INFO",
+                            {
+                                "LOOP": {
+                                    "detected": bool(loop_res.loop_detected),
+                                    "optimized": bool(loop_res.optimized),
+                                    **loop_res.info,
+                                }
+                            },
+                        )
+                    if loop_res.optimized:
+                        corr = np.asarray(loop_res.correction, dtype=np.float64)
+                        # Apply correction to map and reseed filter for consistency.
+                        stable_all = stable.get_points()
+                        corrected = apply_transform_points(stable_all, corr)
+                        if corrected is not None:
+                            stable.set_points(corrected)
+                            if hasattr(filt, "seed_stable_points"):
+                                filt.seed_stable_points(
+                                    corrected,
+                                    now=_now(),
+                                    hit_count=int(filt_cfg.hits_threshold),
+                                    clear_existing=True,
+                                )
+                            stable_pts_display = stable.get_display_points()
+                        loc_frames.apply_loop_correction(corr, update_reference=True)
+                        _sync_loc_vars_from_service()
+                        if last_guard_pose is not None:
+                            last_guard_pose = corr @ np.asarray(last_guard_pose, dtype=np.float64)
+                        if last_key_pose is not None:
+                            last_key_pose = corr @ np.asarray(last_key_pose, dtype=np.float64)
+                        pose = corr @ np.asarray(pose, dtype=np.float64)
+            maybe_auto_localize()
+            maybe_autosave(now_t)
+            if submap_ckpt is not None and submap_mode_enabled:
+                submap_ckpt.maybe_save(submap_mapper)
+        elif localize_only_enabled:
+            now_t = _now()
+            dt = 0.1 if last_frame_t <= 0 else float(np.clip(now_t - last_frame_t, 0.02, 0.25))
+            last_frame_t = now_t
+            ts = np.linspace(0.0, dt, len(pts), dtype=np.float64)
+            t_icp0 = _now()
+            slam.register_frame(pts, ts)
+            perf_icp_ms = 1000.0 * (_now() - t_icp0)
+            odom_pose = slam.last_pose if hasattr(slam, "last_pose") else getattr(slam, "pose", None)
+            if odom_pose is not None:
+                odom_pose, fusion_info = sensor_fusion.fuse_pose(np.asarray(odom_pose, dtype=np.float64), now=now_t)
+                pose = np.asarray(odom_pose, dtype=np.float64)
+            else:
+                pose = None
+
+            live_for_localize = np.asarray(pts, dtype=np.float32)
+            if pose is not None:
+                live_for_localize = to_world_points(pts, pose)
+
+            if loc_live_stack_enabled:
+                frame_ds = voxel_downsample_np(live_for_localize, float(loc_live_stack_voxel_m))
+                if frame_ds is not None and len(frame_ds) > 0:
+                    loc_live_stack.append(np.asarray(frame_ds, dtype=np.float32))
+                if len(loc_live_stack) >= int(loc_live_stack_min_frames):
+                    cat = np.concatenate(list(loc_live_stack), axis=0)
+                    stack_pts = voxel_downsample_np(cat, float(loc_live_stack_voxel_m))
+                    if len(stack_pts) > int(loc_live_stack_max_points):
+                        step = max(2, int(np.ceil(float(len(stack_pts)) / float(loc_live_stack_max_points))))
+                        stack_pts = stack_pts[::step][: int(loc_live_stack_max_points)]
+                    if stack_pts is not None and len(stack_pts) > 0:
+                        live_for_localize = np.asarray(stack_pts, dtype=np.float32)
+
+            latest_live_localize_pts = np.array(live_for_localize, dtype=np.float32, copy=True)
+            maybe_auto_localize(src_live=live_for_localize)
+            loc_state_name = str(loc_state.state).upper().strip()
+            submap_active = bool(
+                submap_mode_enabled
+                and _submap_profile_allowed()
+                and slam_to_ref_valid
+                and loc_state_name in ("TRACKING", "DEGRADED")
+                and float(last_localize_confidence) >= 0.45
+            )
+            if submap_active:
+                if pose is not None and np.asarray(pose).shape == (4, 4):
+                    pose_ref = np.asarray(slam_to_ref_transform, dtype=np.float64) @ np.asarray(pose, dtype=np.float64)
+                    pts_ref = to_world_points(pts, pose_ref)
+                else:
+                    pts_ref = apply_transform_points(pts, slam_to_ref_transform)
+                    pose_ref = (
+                        np.asarray(slam_to_ref_transform, dtype=np.float64)
+                        if np.asarray(slam_to_ref_transform).shape == (4, 4)
+                        else None
+                    )
+                if pts_ref is not None and len(pts_ref) > 0:
+                    submap_info = submap_mapper.integrate(pts_ref, now=now_t, pose_world=pose_ref)
+                    stable_pts_display = submap_mapper.get_display_points()
+                    stable_points_in_global_frame = True
+                else:
+                    stable_pts_display = live_for_localize
+                    submap_info = submap_mapper.status(
+                        active=False,
+                        reason="empty_live",
+                        profile=str(stability_profile),
+                        profile_allowed=True,
+                    )
+            else:
+                if (not submap_mode_enabled) or (not _submap_profile_allowed()) or (not slam_to_ref_valid):
+                    if submap_mapper.has_points and ((not _submap_profile_allowed()) or (not submap_mode_enabled) or (not slam_to_ref_valid)):
+                        submap_mapper.reset()
+                stable_pts_display = live_for_localize
+                submap_info = submap_mapper.status(
+                    active=False,
+                    reason="disabled_or_not_localized",
+                    profile=str(stability_profile),
+                    profile_allowed=_submap_profile_allowed(),
+                )
+            # Keep pose in odom frame; publish path maps it to reference when localized.
+        else:
+            stable_pts_display = stable.get_display_points()
+
+        if recording_enabled:
+            if len(recording_frames) < int(recording_max_frames):
+                recording_frames.append(np.array(pts, dtype=np.float32, copy=True))
+                if pose is not None and np.asarray(pose).shape == (4, 4):
+                    recording_poses.append(np.array(pose, dtype=np.float32, copy=True))
+            else:
+                recording_drop_count += 1
+                recording_enabled = False
+                st(
+                    "WARN",
+                    (
+                        "RECORDING auto-stopped: reached max frames "
+                        f"({int(recording_max_frames)})."
+                    ),
+                )
+
+        # publish throttling
+        t = _now()
+        if pub_hz > 0:
+            if (t - last_pub) < (1.0 / pub_hz):
+                return
+        last_pub = t
+
+        stable_pts_pub = stable_pts_display
+        pose_pub = pose
+        if slam_to_ref_valid and not stable_points_in_global_frame:
+            stable_pts_pub = apply_transform_points(stable_pts_display, slam_to_ref_transform)
+            if pose is not None:
+                try:
+                    pose_pub = slam_to_ref_transform @ np.asarray(pose, dtype=np.float64)
+                except Exception:
+                    pose_pub = pose
+
+        if stable_pts_pub is not None and len(stable_pts_pub) > 0:
+            if localize_only_enabled:
+                # Blue live overlay for map localization workflows.
+                stable_cols = np.zeros((len(stable_pts_pub), 4), dtype=np.float32)
+                stable_cols[:, 0] = 0.18
+                stable_cols[:, 1] = 0.58
+                stable_cols[:, 2] = 1.00
+                stable_cols[:, 3] = 0.72
+            else:
+                alpha_live = 0.90
+                stable_cols = height_colors_fast(stable_pts_pub, alpha=float(alpha_live))
+
+        live_world_for_nav = None
+        if localize_only_enabled:
+            if slam_to_ref_valid:
+                if pose is not None and np.asarray(pose).shape == (4, 4):
+                    try:
+                        pose_ref_live = np.asarray(slam_to_ref_transform, dtype=np.float64) @ np.asarray(pose, dtype=np.float64)
+                        live_world_for_nav = to_world_points(pts, pose_ref_live)
+                    except Exception:
+                        live_world_for_nav = apply_transform_points(pts, slam_to_ref_transform)
+                else:
+                    live_world_for_nav = apply_transform_points(pts, slam_to_ref_transform)
+        else:
+            if pose_pub is not None:
+                live_world_for_nav = to_world_points(pts, np.asarray(pose_pub, dtype=np.float64))
+            elif slam_to_ref_valid:
+                live_world_for_nav = apply_transform_points(pts, slam_to_ref_transform)
+
+        if localize_only_enabled:
+            pose_ref_for_vis = None
+            if slam_to_ref_valid and pose_pub is not None and np.asarray(pose_pub).shape == (4, 4):
+                pose_ref_for_vis = np.asarray(pose_pub, dtype=np.float64)
+            elif slam_to_ref_valid:
+                pose_ref_for_vis = np.asarray(slam_to_ref_transform, dtype=np.float64)
+            ref_match_points_pub, ref_match_colors_pub, ref_match_stats = _build_ref_match_visual(
+                live_world_for_nav,
+                pose_ref_for_vis,
+                _now(),
+            )
+        else:
+            ref_match_stats = {"enabled": bool(loc_vis_enabled), "updated": False, "reason": "not_localize_mode"}
+
+        nav_static_points = stable_pts_pub
+        if localize_only_enabled and ref_points_cache is not None and len(ref_points_cache) > 0:
+            try:
+                src_n = int(len(ref_points_cache))
+                if ref_nav_points_cache is None or int(ref_nav_cache_src_n) != src_n:
+                    nav_voxel = max(0.08, float(nav_rt_cfg.resolution_m) * 1.25)
+                    nav_pts = voxel_downsample_np(np.asarray(ref_points_cache, dtype=np.float32), float(nav_voxel))
+                    if nav_pts is None:
+                        nav_pts = np.zeros((0, 3), dtype=np.float32)
+                    if len(nav_pts) > 300000:
+                        step = max(2, int(np.ceil(float(len(nav_pts)) / 300000.0)))
+                        nav_pts = nav_pts[::step]
+                    ref_nav_points_cache = np.asarray(nav_pts, dtype=np.float32)
+                    ref_nav_cache_src_n = src_n
+                if ref_nav_points_cache is not None and len(ref_nav_points_cache) > 0:
+                    nav_static_points = ref_nav_points_cache
+            except Exception as _nav_exc:
+                st("WARN", f"[nav] static point cache update failed: {_nav_exc}")
+
+        if (t - nav_last_update_t) >= 0.10:
+            nav_runtime_info = nav_runtime.update(nav_static_points, live_world_for_nav, t)
+            nav_last_update_t = t
+
+        mission_snapshot = mission.snapshot()
+        if pose_pub is not None and np.asarray(pose_pub).shape == (4, 4):
+            mission_snapshot = mission.update_pose(float(pose_pub[0, 3]), float(pose_pub[1, 3]))
+            goal_from_mission = mission.current_goal()
+            if goal_from_mission is not None:
+                nav_goal_xy = (float(goal_from_mission[0]), float(goal_from_mission[1]))
+            elif mission_snapshot.get("completed", False):
+                nav_goal_xy = None
+                nav_path_world = []
+
+        if nav_goal_xy is not None and pose_pub is not None and nav_runtime.grid is not None:
+            if (t - nav_last_plan_t) >= float(nav_plan_period_sec) or len(nav_path_world) <= 2:
+                grid = nav_runtime.grid
+                try:
+                    nav_path_world = nav_global_planner.plan(
+                        np.asarray(grid["costmap"], dtype=np.uint8),
+                        np.asarray(grid["origin_xy"], dtype=np.float32),
+                        float(grid["resolution_m"]),
+                        (float(pose_pub[0, 3]), float(pose_pub[1, 3])),
+                        (float(nav_goal_xy[0]), float(nav_goal_xy[1])),
+                    )
+                except Exception as _astar_exc:
+                    st("WARN", f"[nav] A* planning failed: {_astar_exc}")
+                    nav_path_world = []
+                nav_last_plan_t = t
+        elif mission.current_goal() is None:
+            nav_path_world = []
+
+        nav_cmd = {"linear_mps": 0.0, "angular_rps": 0.0, "goal_reached": False, "blocked": False}
+        if pose_pub is not None and len(nav_path_world) > 0:
+            nav_cmd = nav_local_planner.compute_command(np.asarray(pose_pub, dtype=np.float64), nav_path_world, nav_runtime)
+            if bool(nav_cmd.get("goal_reached", False)):
+                mission_snapshot = mission.update_pose(float(pose_pub[0, 3]), float(pose_pub[1, 3]))
+                next_goal = mission.current_goal()
+                nav_goal_xy = None if next_goal is None else (float(next_goal[0]), float(next_goal[1]))
+                nav_path_world = []
+
+        safety_info = safety.evaluate(t, pose_pub, str(loc_state.state), nav_runtime, nav_cmd)
+
+        grid_now = nav_runtime.grid
+        nav_info = {
+            "mode": mode_now,
+            "goal": [float(nav_goal_xy[0]), float(nav_goal_xy[1])] if nav_goal_xy is not None else None,
+            "path_points": int(len(nav_path_world)),
+            "path_preview": (
+                [[float(x), float(y)] for (x, y) in nav_path_world[:: max(1, len(nav_path_world) // 60)]]
+                if len(nav_path_world) > 0
+                else []
+            ),
+            "cmd": dict(safety_info.get("cmd", nav_cmd)),
+            "mission": mission_snapshot,
+            "costmap": (
+                {
+                    "shape": [int(grid_now["shape_hw"][0]), int(grid_now["shape_hw"][1])],
+                    "resolution_m": float(grid_now["resolution_m"]),
+                    "origin_xy": [float(grid_now["origin_xy"][0]), float(grid_now["origin_xy"][1])],
+                    "static_cells": int(grid_now["static_cells"]),
+                    "dynamic_cells": int(grid_now["dynamic_cells"]),
+                    "inflated_cells": int(grid_now["inflated_cells"]),
+                }
+                if isinstance(grid_now, dict)
+                else nav_runtime_info
+            ),
+        }
+
+        stable_count = 0 if stable_pts_pub is None else int(len(stable_pts_pub))
+        _update_perf_rollup(t, stable_count)
+        perf_payload = {
+            "input_fps": float(perf_input_fps),
+            "publish_fps": float(perf_publish_fps),
+            "icp_ms": float(perf_icp_ms),
+            "queue_lag": int(perf_queue_lag),
+            "stable_growth_per_sec": float(perf_stable_growth),
+            "cpu_percent": float(perf_cpu_pct),
+        }
+
+        payload = {
+            "stable_points": stable_pts_pub,
+            "stable_colors": stable_cols,
+            "pose": pose_pub,
+            "filter": filt.stats() if hasattr(filt, "stats") else {},
+            "workflow_profile": str(stability_profile),
+            "map_lock": {
+                "enabled": bool(map_lock_enabled),
+                "accept_count": int(map_lock_accept_count),
+                "reject_count": int(map_lock_reject_count),
+                "rotating_in_place": bool(rotating_in_place) if mapping_enabled else False,
+                "freeze_active": bool(rotate_freeze_active) if mapping_enabled else False,
+                "freeze_count": int(rotate_freeze_count),
+                "last": dict(map_lock_info) if isinstance(map_lock_info, dict) else {},
+            },
+            "continuity": {
+                "enabled": bool(continuity_enabled),
+                "reject_count": int(continuity_reject_count),
+                "reject_streak": int(continuity_consecutive_rejects),
+                "recover_after_rejects": int(continuity_recover_after_rejects),
+                "last": dict(continuity_info) if isinstance(continuity_info, dict) else {},
+            },
+            "submap": dict(submap_info) if isinstance(submap_info, dict) else {},
+            "ref_match_points": ref_match_points_pub,
+            "ref_match_colors": ref_match_colors_pub,
+            "ref_match": dict(ref_match_stats) if isinstance(ref_match_stats, dict) else {},
+            "cleanup": {
+                "enabled": bool(cleanup_enabled),
+                "removed_total": int(cleanup_removed_total),
+                "last": dict(cleanup_info) if isinstance(cleanup_info, dict) else {},
+            },
+            "localized": bool(slam_to_ref_valid),
+            "loc_state": str(loc_state.state),
+            "loc_confidence": float(last_localize_confidence),
+            "fusion": fusion_info,
+            "perf": perf_payload,
+            "mode": str(worker_mode),
+            "nav": nav_info,
+            "safety": safety_info,
+            "recording": {
+                "enabled": bool(recording_enabled),
+                "frames": int(len(recording_frames)),
+                "dropped": int(recording_drop_count),
+            },
+        }
+        perf_publish_count += 1
+        _safe_put(data_q, ("FRAME", payload), keep_latest=bool(run_cfg.frame_keep_latest))
+
+    # ---------------- lidar callback (lightweight) ----------------
+    # Keep SDK callback minimal: enqueue latest frame and process in worker loop.
+    # ---------------- IMU orientation integrator ----------------
+    # Complementary filter: gyro integrates at full 200 Hz; accelerometer
+    # corrects roll/pitch drift at a low gain (alpha ≈ 0.97).
+    # Yaw is gyro-only (no magnetometer) — drifts slowly, but far less than
+    # KISS-ICP yaw on fast rotations.
+    # Only used when fusion.enabled = true in config.
+
+    class _ImuIntegrator:
+        __slots__ = ("R", "last_t", "alpha", "last_pose_translation")
+
+        def __init__(self, alpha: float = 0.97) -> None:
+            self.R = np.eye(3, dtype=np.float64)
+            self.last_t: float = 0.0
+            self.alpha = float(np.clip(alpha, 0.80, 0.999))
+            self.last_pose_translation = np.zeros(3, dtype=np.float64)
+
+        def reset(self) -> None:
+            self.R = np.eye(3, dtype=np.float64)
+            self.last_t = 0.0
+            self.last_pose_translation[:] = 0.0
+
+        def update(self, gyro: np.ndarray, acc: np.ndarray, t: float) -> np.ndarray:
+            """Returns a 4×4 pose. Translation comes from last known LiDAR pose."""
+            if self.last_t <= 0.0:
+                self.last_t = t
+                return np.eye(4, dtype=np.float64)
+            dt = float(t - self.last_t)
+            if dt <= 0.0 or dt > 0.5:
+                self.last_t = t
+                return np.eye(4, dtype=np.float64)
+            self.last_t = t
+
+            # 1 — Gyro integration via Rodrigues
+            omega = np.asarray(gyro, dtype=np.float64)
+            angle = float(np.linalg.norm(omega)) * dt
+            if angle > 1e-9:
+                axis = omega / (float(np.linalg.norm(omega)) + 1e-15)
+                K = np.array([[0.0, -axis[2], axis[1]],
+                               [axis[2], 0.0, -axis[0]],
+                               [-axis[1], axis[0], 0.0]], dtype=np.float64)
+                dR = np.eye(3, dtype=np.float64) + np.sin(angle) * K + (1.0 - np.cos(angle)) * (K @ K)
+                self.R = self.R @ dR
+
+            # 2 — Accelerometer correction for roll/pitch (complementary filter)
+            a = np.asarray(acc, dtype=np.float64)
+            a_norm = float(np.linalg.norm(a))
+            if 7.5 < a_norm < 12.5:   # only trust near 1 g; reject dynamic spikes
+                a_unit = a / a_norm
+                g_in_sensor = self.R.T @ np.array([0.0, 0.0, -1.0])  # expected gravity direction
+                cross = np.cross(a_unit, g_in_sensor)
+                cross_norm = float(np.linalg.norm(cross))
+                if cross_norm > 1e-6:
+                    corr_angle = float(np.arcsin(np.clip(cross_norm, -1.0, 1.0))) * (1.0 - self.alpha)
+                    axis_c = cross / cross_norm
+                    Kc = np.array([[0.0, -axis_c[2], axis_c[1]],
+                                    [axis_c[2], 0.0, -axis_c[0]],
+                                    [-axis_c[1], axis_c[0], 0.0]], dtype=np.float64)
+                    dR_c = np.eye(3, dtype=np.float64) + np.sin(corr_angle) * Kc + (1.0 - np.cos(corr_angle)) * (Kc @ Kc)
+                    self.R = dR_c @ self.R
+
+            # Re-orthogonalise to prevent numerical drift
+            try:
+                U, _, Vt = np.linalg.svd(self.R, full_matrices=False)
+                self.R = U @ Vt
+            except np.linalg.LinAlgError:
+                self.R = np.eye(3, dtype=np.float64)
+
+            T = np.eye(4, dtype=np.float64)
+            T[:3, :3] = self.R
+            T[:3, 3] = self.last_pose_translation   # inject LiDAR translation so fusion doesn't shift position
+            return T
+
+    _imu_integrator = _ImuIntegrator(alpha=float(
+        ((full_cfg.get("fusion", {}) or {}).get("imu_complementary_alpha", 0.97))
+    ))
+
+    def on_imu(gyro: np.ndarray, acc: np.ndarray, timestamp: float) -> None:
+        if not fusion_cfg.enabled:
+            return
+        try:
+            # Keep the integrator's translation in sync with the latest LiDAR pose
+            with state_lock:
+                cur_tf = np.array(odom_to_map_transform, dtype=np.float64, copy=True)
+            _imu_integrator.last_pose_translation = cur_tf[:3, 3].copy()
+            imu_pose = _imu_integrator.update(gyro, acc, timestamp)
+            sensor_fusion.update_prior(
+                sensor="imu",
+                pose=imu_pose,
+                confidence=0.85,
+                timestamp=timestamp,
+            )
+        except Exception as _imu_exc:
+            pass   # IMU is advisory; never let it crash the worker
+
+    # This avoids running heavy ICP/Open3D inside the Livox SDK thread.
+    def on_points(points: np.ndarray):
+        if points is None or len(points) == 0:
+            return
+        try:
+            pts = np.asarray(points, dtype=np.float32)
+            if pts.ndim != 2 or pts.shape[1] != 3:
+                return
+        except Exception:
+            return
+        _safe_put(raw_points_q, np.array(pts, dtype=np.float32, copy=True), keep_latest=True)
+
+    # ---------------- commands ----------------
+    def do_connect():
+        nonlocal lidar, connected
+        if connected:
+            st("INFO", "Already connected.")
+            return
+
+        cfg_path = Path(eng_cfg.config_file)
+        if not cfg_path.exists():
+            st("ERROR", f"Config missing: {cfg_path}")
+            return
+
+        try:
+            lidar = Livox2(
+                str(cfg_path),
+                host_ip=str(eng_cfg.host_ip),
+                debug=bool(livox_debug_enabled),
+                print_every_n_frames=int(livox_print_every_n),
+                tag_filter=bool(getattr(eng_cfg, "tag_filter", True)),
+            )
+            lidar.handle_points = on_points
+            lidar.handle_imu = on_imu
+            connected = True
+            st("INFO", f"CONNECTED to Livox (host_ip={eng_cfg.host_ip})")
+        except Exception as e:
+            connected = False
+            err = str(e)
+            up = err.upper()
+            if ("SDKINIT" in up) or ("BIND" in up) or ("SOCKET" in up) or ("ADDRESS ALREADY IN USE" in up):
+                err = (
+                    f"{err}\n"
+                    "Hint: likely UDP port conflict or wrong host IP.\n"
+                    "1) Close other Livox/SLAM apps using ports 56101-56501.\n"
+                    "2) Ensure GUI host IP matches this PC NIC on LiDAR network.\n"
+                    "3) Verify mid360_config.json host_net_info *_ip and *_port values."
+                )
+            st("ERROR", f"Connect failed: {err}")
+
+    def do_start():
+        nonlocal mapping_enabled, localize_only_enabled, worker_mode
+        nonlocal last_guard_pose, last_guard_t, guard_reject_count
+        nonlocal continuity_prev_pts, continuity_reject_count, continuity_consecutive_rejects, continuity_last_warn_t
+        nonlocal rotate_freeze_count, rotate_last_warn_t
+        nonlocal cleanup_last_t, cleanup_last_info
+        nonlocal last_frame_t, loc_live_stack, latest_live_localize_pts
+        if not connected:
+            st("ERROR", "START denied: not connected.")
+            return
+        if self_check_report.get("errors"):
+            st("ERROR", "START denied: startup self-check has errors. Fix config paths first.")
+            return
+        mapping_enabled = True
+        localize_only_enabled = False
+        last_frame_t = 0.0
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        last_guard_pose = None
+        last_guard_t = 0.0
+        guard_reject_count = 0
+        continuity_prev_pts = None
+        continuity_reject_count = 0
+        continuity_consecutive_rejects = 0
+        continuity_last_warn_t = 0.0
+        rotate_freeze_count = 0
+        rotate_last_warn_t = 0.0
+        cleanup_last_t = 0.0
+        cleanup_last_info = {"status": "na"}
+        if not _submap_profile_allowed():
+            submap_mapper.reset()
+        worker_mode = "MAPPING"
+        st("INFO", "MAPPING STARTED.")
+        push_worker_mode()
+
+    def do_start_localize_only():
+        nonlocal mapping_enabled, localize_only_enabled, worker_mode, last_localize_t, last_localize_confidence
+        nonlocal last_key_pose, last_guard_pose, last_guard_t, guard_reject_count, last_frame_t
+        nonlocal odom_to_map_transform, continuity_prev_pts, continuity_consecutive_rejects
+        nonlocal continuity_reject_count, rotate_freeze_count, loc_live_stack
+        nonlocal slam_to_ref_valid, latest_live_localize_pts
+        if not connected:
+            st("ERROR", "LOCALIZE_ONLY denied: not connected.")
+            return
+        if not ref_map_path:
+            st("ERROR", "LOCALIZE_ONLY denied: load reference map first.")
+            return
+        # Start localize-only from a fresh odometry frame to avoid stale-map offsets.
+        init_slam_stack()
+        mapping_enabled = False
+        localize_only_enabled = True
+        last_frame_t = 0.0
+        loc_frames.reset()
+        _sync_loc_vars_from_service()
+        last_key_pose = None
+        last_guard_pose = None
+        last_guard_t = 0.0
+        guard_reject_count = 0
+        continuity_prev_pts = None
+        continuity_consecutive_rejects = 0
+        continuity_reject_count = 0
+        rotate_freeze_count = 0
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        submap_mapper.reset()
+        worker_mode = "LOCALIZE_ONLY"
+        last_localize_t = 0.0
+        st("INFO", "LOCALIZE_ONLY STARTED.")
+        push_worker_mode()
+
+    def do_pause():
+        nonlocal mapping_enabled, localize_only_enabled, worker_mode
+        nonlocal loc_live_stack, latest_live_localize_pts
+        mapping_enabled = False
+        localize_only_enabled = False
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        worker_mode = "PAUSED"
+        st("INFO", "MAPPING PAUSED.")
+        push_worker_mode()
+
+    def do_stop():
+        nonlocal mapping_enabled, localize_only_enabled, worker_mode
+        nonlocal loc_live_stack, latest_live_localize_pts
+        mapping_enabled = False
+        localize_only_enabled = False
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        worker_mode = "STOPPED"
+        st("INFO", "MAPPING STOPPED.")
+        push_worker_mode()
+
+    def do_export(filename_base: str):
+        try:
+            # Final cleanup pass: flush any stray islands that arrived since the last
+            # periodic cleanup (which runs every cleanup_period_sec seconds).
+            if cleanup_enabled:
+                full_pts = stable.get_points()
+                if full_pts is not None and len(full_pts) >= int(cleanup_min_total_points):
+                    cleaned, _ = cleanup_map_islands(full_pts)
+                    if cleaned is not None and len(cleaned) >= int(map_cfg.min_points_to_save):
+                        stable.set_points(cleaned)
+            pts = stable.get_save_points()
+            n = 0 if pts is None else len(pts)
+            req = int(map_cfg.min_points_to_save)
+            if pts is None or n < req:
+                st("ERROR", f"EXPORT refused: stable points {n}/{req}. Keep mapping and try again.")
+                return
+            out_path = stable.export_map(filename_base)
+            st("INFO", f"SAVED: {out_path}")
+        except Exception as e:
+            st("ERROR", f"EXPORT failed: {e}")
+
+    def do_export_nav(filename_base: str):
+        try:
+            pts = stable.get_save_points()
+            n_total = 0 if pts is None else len(pts)
+            req = int(nav_cfg.min_points)
+            if pts is None or n_total < req:
+                st("ERROR", f"EXPORT_NAV refused: stable points {n_total}/{req}.")
+                return
+            n_band = int(nav_exporter.count_nav_points(pts))
+            if n_band < req:
+                st(
+                    "ERROR",
+                    (
+                        "EXPORT_NAV refused: nav-band points "
+                        f"{n_band}/{req} in z[{float(nav_cfg.z_min_m):.2f}, {float(nav_cfg.z_max_m):.2f}] m."
+                    ),
+                )
+                return
+            result = nav_exporter.export(filename_base, pts)
+            st("INFO", {"NAV_EXPORTED": result})
+        except Exception as e:
+            st("ERROR", f"EXPORT_NAV failed: {e}")
+
+    def do_load_ref(path_value: str):
+        nonlocal ref_map_path, ref_pcd_cache, ref_points_cache, ref_nav_points_cache, ref_nav_cache_src_n, ref_cache_path
+        nonlocal last_localize_t, last_localize_points, last_localize_transform, last_localize_confidence
+        nonlocal slam_to_ref_transform, slam_to_ref_valid
+        nonlocal approx_pose_guess_tf, approx_pose_guess_until_t
+        nonlocal approx_pose_guess_fail_count, approx_pose_guess_success_count
+        nonlocal ref_match_points_cache, ref_match_confirm_hits, ref_match_last_pub_t, ref_match_last_stats
+        nonlocal loc_live_stack, last_frame_t, latest_live_localize_pts
+        ref_map_path = str(path_value)
+        ref_pcd_cache = None
+        ref_points_cache = None
+        ref_nav_points_cache = None
+        ref_nav_cache_src_n = 0
+        ref_cache_path = None
+        ref_match_points_cache = None
+        ref_match_confirm_hits = None
+        ref_match_last_pub_t = 0.0
+        ref_match_last_stats = {}
+        place_recog.reset()
+        last_frame_t = 0.0
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        submap_mapper.reset()
+        last_localize_t = 0.0
+        last_localize_points = 0
+        loc_frames.reset_reference()
+        _sync_loc_vars_from_service()
+        approx_pose_guess_tf = None
+        approx_pose_guess_until_t = 0.0
+        approx_pose_guess_fail_count = 0
+        approx_pose_guess_success_count = 0
+        loc_state.reset()
+        st("INFO", f"REF loaded: {Path(ref_map_path).name}")
+        st("INFO", {"PLACE_RECOG": place_recog.snapshot()})
+        prior_tf = session_store.get_transform(ref_map_path)
+        if prior_tf is not None:
+            loc_frames.last_alignment = np.array(prior_tf, dtype=np.float64, copy=True)
+            _sync_loc_vars_from_service()
+            st("INFO", {"SESSION": {"prior_loaded": True, "ref": Path(ref_map_path).name}})
+        # In map-localization workflows we wait for fresh live scans after START,
+        # instead of trying to localize immediately from stale/non-live points.
+        if mapping_enabled and (not localize_only_enabled):
+            do_localize(force=False, source="ON_LOAD")
+        push_loc_state()
+
+    def do_manual_localize():
+        src_live = latest_live_localize_pts if localize_only_enabled else None
+        if localize_only_enabled and (src_live is None or int(len(src_live)) < 80):
+            st("INFO", "LOCALIZE: waiting for live scan...")
+            return
+        do_localize(force=True, source="MANUAL", src_override=src_live)
+
+    def do_load_for_extend(path_value: str):
+        """Load a saved .ply AND seed the stable map + filter so the next
+        START extends the loaded map instead of building one from scratch.
+
+        Implementation:
+          1. Reuses do_load_ref() so localization can still anchor to the
+             loaded map's frame.
+          2. Reads the .ply into numpy via open3d.
+          3. Seeds stable.set_points(...) so the GUI shows the loaded map
+             immediately.
+          4. Seeds the persistence filter with hit_count = current threshold
+             so the loaded points count as "already stable" — incoming
+             scans add to them rather than re-build them.
+        """
+        if not str(path_value):
+            st("ERROR", "EXTEND denied: no path provided.")
+            return
+        do_load_ref(str(path_value))
+        try:
+            import open3d as o3d  # local import — open3d is heavy
+            pcd = o3d.io.read_point_cloud(str(path_value))
+            pts = np.asarray(pcd.points, dtype=np.float32)
+        except Exception as e:
+            st("ERROR", f"EXTEND load failed: {e}")
+            return
+        if pts is None or len(pts) == 0:
+            st("ERROR", "EXTEND denied: loaded file has no points.")
+            return
+        try:
+            stable.set_points(pts)
+            if hasattr(filt, "seed_stable_points"):
+                filt.seed_stable_points(
+                    pts,
+                    now=_now(),
+                    hit_count=int(runtime_filter_hit_threshold),
+                    clear_existing=True,
+                )
+        except Exception as e:
+            st("WARN", f"EXTEND seed partial failure: {e}")
+        st("INFO", {"EXTEND_LOAD": {"path": str(path_value), "points": int(len(pts))}})
+
+    def do_clear_ref():
+        nonlocal ref_map_path, ref_pcd_cache, ref_points_cache, ref_nav_points_cache, ref_nav_cache_src_n, ref_cache_path
+        nonlocal last_localize_t, last_localize_points, last_localize_transform, last_localize_confidence
+        nonlocal slam_to_ref_transform, slam_to_ref_valid
+        nonlocal localize_only_enabled, worker_mode
+        nonlocal approx_pose_guess_tf, approx_pose_guess_until_t
+        nonlocal approx_pose_guess_fail_count, approx_pose_guess_success_count
+        nonlocal ref_match_points_cache, ref_match_confirm_hits, ref_match_last_pub_t, ref_match_last_stats
+        nonlocal loc_live_stack, last_frame_t, latest_live_localize_pts
+        ref_map_path = None
+        ref_pcd_cache = None
+        ref_points_cache = None
+        ref_nav_points_cache = None
+        ref_nav_cache_src_n = 0
+        ref_cache_path = None
+        ref_match_points_cache = None
+        ref_match_confirm_hits = None
+        ref_match_last_pub_t = 0.0
+        ref_match_last_stats = {}
+        place_recog.reset()
+        last_frame_t = 0.0
+        loc_live_stack.clear()
+        latest_live_localize_pts = None
+        submap_mapper.reset()
+        last_localize_t = 0.0
+        last_localize_points = 0
+        loc_frames.reset_reference()
+        _sync_loc_vars_from_service()
+        approx_pose_guess_tf = None
+        approx_pose_guess_until_t = 0.0
+        approx_pose_guess_fail_count = 0
+        approx_pose_guess_success_count = 0
+        localize_only_enabled = False
+        if not mapping_enabled:
+            worker_mode = "IDLE"
+        loc_state.reset()
+        st("INFO", "REF cleared.")
+        push_worker_mode()
+        push_loc_state()
+
+    st("INFO", "Worker ready. CONNECT then START.")
+    st("INFO", {"SELF_CHECK": self_check_report, "source": "worker"})
+    apply_density_mode("MEDIUM")
+    apply_stability_profile("BALANCED")
+    apply_min_stable_points(map_cfg.min_points_to_save)
+    apply_loop_closure_mode(loop_cfg.enabled)
+    apply_loc_state_machine_mode(loc_state_cfg.enabled)
+    apply_submap_mode(
+        {
+            "enabled": submap_mode_enabled,
+            "local_window_frames": submap_cfg.local_window_frames,
+            "local_voxel_m": submap_cfg.local_voxel_m,
+            "global_voxel_m": submap_cfg.global_voxel_m,
+            "merge_period_sec": submap_cfg.merge_period_sec,
+            "merge_min_translation_m": submap_cfg.merge_min_translation_m,
+            "merge_min_rotation_deg": submap_cfg.merge_min_rotation_deg,
+            "max_global_points": submap_cfg.max_global_points,
+            "display_max_points": submap_cfg.display_max_points,
+            "apply_profiles": list(submap_cfg.apply_profiles),
+            "keep_points": False,
+        }
+    )
+    apply_autosave(
+        {
+            "enabled": autosave_enabled,
+            "interval_sec": autosave_interval_sec,
+            "base_name": autosave_base_name,
+        }
+    )
+    apply_nav_config(
+        {
+            "min_points": nav_cfg.min_points,
+            "resolution_m": nav_cfg.resolution_m,
+            "z_min_m": nav_cfg.z_min_m,
+            "z_max_m": nav_cfg.z_max_m,
+            "inflation_radius_m": nav_cfg.inflation_radius_m,
+            "padding_m": nav_cfg.padding_m,
+        }
+    )
+    apply_map_quality_config(
+        {
+            "enabled": map_quality_cfg.enabled,
+            "near_min_range_m": map_quality_cfg.near_min_range_m,
+            "ray_consistency_enabled": map_quality_cfg.ray_consistency_enabled,
+            "ray_bin_deg": map_quality_cfg.ray_bin_deg,
+            "ray_elev_bin_deg": map_quality_cfg.ray_elev_bin_deg,
+            "ray_max_behind_m": map_quality_cfg.ray_max_behind_m,
+            "ray_keep_n": map_quality_cfg.ray_keep_n,
+            "world_z_clip_enabled": map_quality_cfg.world_z_clip_enabled,
+            "world_z_min_m": map_quality_cfg.world_z_min_m,
+            "world_z_max_m": map_quality_cfg.world_z_max_m,
+            "outlier_filter_enabled": map_quality_cfg.outlier_filter_enabled,
+            "outlier_voxel_m": map_quality_cfg.outlier_voxel_m,
+            "outlier_min_points": map_quality_cfg.outlier_min_points,
+        }
+    )
+    apply_nav_runtime_config(
+        {
+            "enabled": nav_rt_cfg.enabled,
+            "resolution_m": nav_rt_cfg.resolution_m,
+            "z_min_m": nav_rt_cfg.z_min_m,
+            "z_max_m": nav_rt_cfg.z_max_m,
+            "padding_m": nav_rt_cfg.padding_m,
+            "inflation_radius_m": nav_rt_cfg.inflation_radius_m,
+            "dynamic_decay_sec": nav_rt_cfg.dynamic_decay_sec,
+            "dynamic_min_hits": nav_rt_cfg.dynamic_min_hits,
+            "blocked_cost": nav_rt_cfg.blocked_cost,
+        }
+    )
+    st("INFO", {"MISSION": mission.snapshot()})
+    push_loc_state()
+    push_worker_mode()
+
+    while running:
+        try:
+            cmd = None
+            try:
+                cmd = cmd_q.get(timeout=0.02)
+            except queue.Empty:
+                cmd = None
+
+            if cmd:
+                if isinstance(cmd, tuple):
+                    name = str(cmd[0]).upper().strip()
+                    payload = cmd[1] if len(cmd) > 1 else None
+                else:
+                    name = str(cmd).upper().strip()
+                    payload = None
+
+                if name == "CONNECT":
+                    _run_locked(do_connect)
+                elif name == "START":
+                    _run_locked(do_start)
+                elif name == "START_LOCALIZE_ONLY":
+                    _run_locked(do_start_localize_only)
+                elif name == "STOP_LOCALIZE_ONLY":
+                    _run_locked(do_stop)
+                elif name == "PAUSE":
+                    _run_locked(do_pause)
+                elif name == "STOP":
+                    _run_locked(do_stop)
+                elif name == "RESET":
+                    _run_locked(reset_all)
+                elif name == "EXPORT":
+                    _run_locked(do_export, str(payload or "map_robot"))
+                elif name == "EXPORT_NAV":
+                    _run_locked(do_export_nav, str(payload or "map_robot"))
+                elif name == "LOAD_REF":
+                    _run_locked(do_load_ref, str(payload))
+                elif name == "LOAD_FOR_EXTEND":
+                    _run_locked(do_load_for_extend, str(payload))
+                elif name == "LOCALIZE":
+                    _run_locked(do_manual_localize)
+                elif name == "CLEAR_REF":
+                    _run_locked(do_clear_ref)
+                elif name == "SET_DENSITY":
+                    _run_locked(apply_density_mode, str(payload or "MEDIUM"))
+                elif name == "SET_MIN_STABLE_POINTS":
+                    _run_locked(apply_min_stable_points, payload)
+                elif name == "SET_LOOP_CLOSURE":
+                    _run_locked(apply_loop_closure_mode, payload)
+                elif name == "SET_LOC_STATE_MACHINE":
+                    _run_locked(apply_loc_state_machine_mode, payload)
+                elif name == "SET_SUBMAP_MODE":
+                    _run_locked(apply_submap_mode, payload)
+                elif name == "SET_APPROX_POSE":
+                    _run_locked(apply_approx_pose, payload if isinstance(payload, dict) else None)
+                elif name == "CLEAR_APPROX_POSE":
+                    _run_locked(apply_approx_pose, None)
+                elif name == "SET_AUTOSAVE":
+                    _run_locked(apply_autosave, payload if isinstance(payload, dict) else {})
+                elif name == "SET_NAV_CONFIG":
+                    _run_locked(apply_nav_config, payload if isinstance(payload, dict) else {})
+                elif name == "SET_NAV_RUNTIME":
+                    _run_locked(apply_nav_runtime_config, payload if isinstance(payload, dict) else {})
+                elif name == "SET_NAV_GOAL":
+                    _run_locked(apply_nav_goal, payload if isinstance(payload, dict) else {})
+                elif name == "CLEAR_NAV_GOAL":
+                    _run_locked(clear_nav_goal)
+                elif name == "SET_MAP_QUALITY":
+                    _run_locked(apply_map_quality_config, payload if isinstance(payload, dict) else {})
+                elif name == "SET_FILTER_TUNING":
+                    _run_locked(apply_filter_tuning, payload if isinstance(payload, dict) else {})
+                elif name == "SET_STABILITY_PROFILE":
+                    _run_locked(apply_stability_profile, payload)
+                elif name == "RECORD_START":
+                    _run_locked(record_start, payload if isinstance(payload, dict) else {})
+                elif name == "RECORD_STOP":
+                    _run_locked(record_stop, payload if isinstance(payload, dict) else {})
+                elif name == "MISSION_START":
+                    _run_locked(mission_start, payload if isinstance(payload, dict) else {})
+                elif name == "MISSION_PAUSE":
+                    _run_locked(mission_pause)
+                elif name == "MISSION_RESUME":
+                    _run_locked(mission_resume)
+                elif name == "MISSION_STOP":
+                    _run_locked(mission_stop)
+                elif name == "SENSOR_PRIOR":
+                    if isinstance(payload, dict):
+                        def _apply_sensor_prior():
+                            ok = sensor_fusion.update_prior(
+                                sensor=str(payload.get("sensor", "unknown")),
+                                pose=payload.get("pose"),
+                                confidence=float(payload.get("confidence", 1.0)),
+                                timestamp=float(payload.get("timestamp", _now())),
+                            )
+                            if not ok:
+                                st("WARN", "SENSOR_PRIOR ignored: invalid payload.")
+                        _run_locked(_apply_sensor_prior)
+                    else:
+                        st("WARN", "SENSOR_PRIOR ignored: payload must be dict.")
+                elif name == "SHUTDOWN":
+                    running = False
+                else:
+                    st("WARN", f"Unknown command: {name}")
+
+            latest_pts = None
+            try:
+                while True:
+                    latest_pts = raw_points_q.get_nowait()
+            except queue.Empty:
+                pass
+
+            if latest_pts is not None:
+                _run_locked(process_points, latest_pts)
+
+        except KeyboardInterrupt:
+            break
+        except Exception as e:
+            if diagnostics is not None:
+                diagnostics.log_exception("Worker loop error", e, _worker_diag_state())
+            st("ERROR", f"Loop error: {e}\n{traceback.format_exc()}")
+
+    try:
+        if lidar is not None and hasattr(lidar, "shutdown"):
+            lidar.shutdown()
+    except Exception:
+        pass
+
+    try:
+        if recording_enabled and len(recording_frames) > 0:
+            record_stop({"save": True, "base_name": recording_base_name})
+    except Exception:
+        pass
+
+    try:
+        if diagnostics is not None:
+            diagnostics.close()
+    except Exception:
+        pass
+
+    st("INFO", "Worker shutdown.")
diff --git a/Lidar/.bak-before-prod-sync/livox2_python.py b/Lidar/.bak-before-prod-sync/livox2_python.py
new file mode 100644
index 0000000..4a809c2
--- /dev/null
+++ b/Lidar/.bak-before-prod-sync/livox2_python.py
@@ -0,0 +1,367 @@
+"""Wrapper for Livox-SDK **2** (push-mode, no broadcast).
+
+Tested against Livox-SDK2 1.2.x – build it first:
+
+    git clone https://github.com/Livox-SDK/Livox-SDK2.git
+    cd Livox-SDK2 && mkdir build && cd build
+    cmake .. -DCMAKE_BUILD_TYPE=Release && make -j$(nproc)
+    sudo make install          # installs liblivox_lidar_sdk.so → /usr/local/lib
+
+Create a JSON config (see livox_lidar_quick_start/mid360_config.json) that
+points the LiDAR to *your* host-IP (192.168.123.222) and save it e.g.
+as ``mid360_config.json`` in this repo.  Pass that path to ``Livox2``.
+"""
+
+from __future__ import annotations
+
+import ctypes as _C
+import json
+import os
+import sys
+import threading
+import time
+from ctypes import (
+    POINTER,
+    c_char_p,
+    c_uint8,
+    c_uint16,
+    c_uint32,
+    c_float,
+    c_bool,
+)
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+
+# ---------------- dynamic library ------------------------------------------------
+
+
+def _load_lib():
+    for name in (
+        "liblivox_lidar_sdk_shared.so",
+        "liblivox_lidar_sdk.so",
+        "livox_lidar_sdk.dll",  # Windows
+    ):
+        try:
+            return _C.cdll.LoadLibrary(name)
+        except OSError:
+            continue
+    raise OSError(
+        "liblivox_lidar_sdk shared library not found.  Build & install "
+        "Livox-SDK2 first (see wrapper docstring)."
+    )
+
+
+_lib = _load_lib()
+
+# ---------------- ctypes mapping --------------------------------------------------
+
+
+class _LivoxLidarEthernetPacket(_C.Structure):
+    _pack_ = 1
+    _fields_ = [
+        ("version", c_uint8),
+        ("length", c_uint16),
+        ("time_interval", c_uint16),
+        ("dot_num", c_uint16),
+        ("udp_cnt", c_uint16),
+        ("frame_cnt", c_uint8),
+        ("data_type", c_uint8),
+        ("time_type", c_uint8),
+        ("rsvd", c_uint8 * 12),
+        ("crc32", c_uint32),
+        ("timestamp", c_uint8 * 8),
+        ("data", c_uint8 * 1),
+    ]
+
+
+class _ImuRawPoint(_C.Structure):
+    """MID-360 IMU sample (data_type == 0).  200 Hz, 6-DOF BMI088."""
+    _pack_ = 1
+    _fields_ = [
+        ("gyro_x",  c_float),   # rad/s
+        ("gyro_y",  c_float),
+        ("gyro_z",  c_float),
+        ("acc_x",   c_float),   # m/s²
+        ("acc_y",   c_float),
+        ("acc_z",   c_float),
+    ]
+
+
+class _CartesianHighPoint(_C.Structure):
+    _pack_ = 1
+    _fields_ = [
+        ("x", _C.c_int32),
+        ("y", _C.c_int32),
+        ("z", _C.c_int32),
+        ("reflectivity", c_uint8),
+        ("tag", c_uint8),
+    ]
+
+
+# Callback typedef
+_PointCb = _C.CFUNCTYPE(None, c_uint32, c_uint8, POINTER(_LivoxLidarEthernetPacket), _C.c_void_p)
+
+# Info change callback
+class _LivoxLidarInfo(_C.Structure):
+    _fields_ = [
+        ("dev_type", c_uint8),
+        ("sn", _C.c_char * 16),
+        ("lidar_ip", _C.c_char * 16),
+    ]
+
+
+_InfoChangeCb = _C.CFUNCTYPE(None, c_uint32, POINTER(_LivoxLidarInfo), _C.c_void_p)
+
+# ---------------------------------------------------------------------------
+# Additional API we use for push-mode
+# ---------------------------------------------------------------------------
+
+
+_lib.SetLivoxLidarInfoChangeCallback.argtypes = (_InfoChangeCb, _C.c_void_p)
+
+_lib.SetLivoxLidarWorkMode.argtypes = (c_uint32, c_uint8, _C.c_void_p, _C.c_void_p)
+_lib.SetLivoxLidarWorkMode.restype = c_uint32
+
+_lib.EnableLivoxLidarPointSend.argtypes = (c_uint32, _C.c_void_p, _C.c_void_p)
+_lib.EnableLivoxLidarPointSend.restype = c_uint32
+
+_lib.SetLivoxLidarPclDataType.argtypes = (c_uint32, c_uint8, _C.c_void_p, _C.c_void_p)
+
+# Point-cloud observer (interface side; lets SDK join multicast)
+_lib.LivoxLidarAddPointCloudObserver.argtypes = (_PointCb, _C.c_void_p)
+_lib.LivoxLidarAddPointCloudObserver.restype = c_uint16
+
+# ---------------- function prototypes -------------------------------------------
+
+
+_lib.LivoxLidarSdkInit.argtypes = (c_char_p, c_char_p, _C.c_void_p)
+_lib.LivoxLidarSdkInit.restype = c_bool
+
+_lib.LivoxLidarSdkStart.argtypes = ()
+_lib.LivoxLidarSdkStart.restype = c_bool
+
+_lib.LivoxLidarSdkUninit.argtypes = ()
+_lib.LivoxLidarSdkUninit.restype = None
+
+_lib.SetLivoxLidarPointCloudCallBack.argtypes = (_PointCb, _C.c_void_p)
+
+# ---------------- Pythonic wrapper ----------------------------------------------
+
+
+class Livox2:
+    """Minimal wrapper around Livox-SDK2 push-mode pipeline."""
+
+    def __init__(self, config_path: str | Path, host_ip: str,
+                 *, frame_time: float = 0.20, frame_packets: int = 120,
+                 debug: bool = True, print_every_n_frames: int = 1,
+                 tag_filter: bool = True):
+        self._config_path = os.fspath(config_path).encode()
+        self._tag_filter = bool(tag_filter)
+
+        if not _lib.LivoxLidarSdkInit(self._config_path, host_ip.encode(), None):
+            raise RuntimeError("LivoxLidarSdkInit failed – check config path & JSON")
+
+        # Register callback *before* starting threads (matches vendor sample)
+        self._cb = _PointCb(self._on_packet)
+        _lib.SetLivoxLidarPointCloudCallBack(self._cb, None)
+
+        # start SDK threads
+        _lib.LivoxLidarSdkStart()
+
+        # Register info-change callback to learn lidar handle once, then start it.
+        self._info_cb = _InfoChangeCb(self._on_info_change)
+        _lib.SetLivoxLidarInfoChangeCallback(self._info_cb, None)
+
+        self._running = True
+
+        # Aggregation parameters for pseudo-frames
+        self._frame_time = float(frame_time)
+        self._frame_packets = int(frame_packets)
+        self._debug = bool(debug)
+        self._print_every_n_frames = max(1, int(print_every_n_frames))
+        self._frame_print_counter = 0
+        self._frame_state: dict = {}
+        self._frame_lock = threading.Lock()
+
+    # ------------------------------------------------------------------
+    def spin(self):
+        try:
+            while self._running:
+                time.sleep(0.01)
+        except KeyboardInterrupt:
+            pass
+        finally:
+            self.shutdown()
+
+    def shutdown(self):
+        if self._running:
+            _lib.LivoxLidarSdkUninit()
+            self._running = False
+
+    # ------------------------------------------------------------------
+    def handle_points(self, xyz: np.ndarray):  # noqa: D401
+        if self._debug:
+            print(f"frame {len(xyz)} pts")
+
+    def handle_imu(self, gyro: np.ndarray, acc: np.ndarray, timestamp: float) -> None:  # noqa: D401
+        """Override to receive IMU data.
+
+        Parameters
+        ----------
+        gyro      : (3,) float32 – angular velocity in rad/s (x, y, z)
+        acc       : (3,) float32 – linear acceleration in m/s² (x, y, z)
+        timestamp : float – time.time() when packet was received
+        """
+
+    # ------------------------------------------------------------------
+    def _on_packet(self, handle: int, dev_type: int, pkt_ptr, _client):
+        pkt = pkt_ptr.contents
+        n = pkt.dot_num
+        if n == 0:
+            return
+
+        if pkt.data_type == 0:  # IMU samples (200 Hz, BMI088)
+            _ArrI = _ImuRawPoint * n
+            samples = _C.cast(pkt.data, POINTER(_ArrI)).contents
+            arr = np.ctypeslib.as_array(samples)
+            # Average all samples in the packet (typically 1–5 at 200 Hz)
+            gyro = np.array(
+                [arr["gyro_x"].mean(), arr["gyro_y"].mean(), arr["gyro_z"].mean()],
+                dtype=np.float32,
+            )
+            acc = np.array(
+                [arr["acc_x"].mean(), arr["acc_y"].mean(), arr["acc_z"].mean()],
+                dtype=np.float32,
+            )
+            try:
+                self.handle_imu(gyro, acc, time.time())
+            except Exception as exc:
+                print(f"[Livox2] Exception in handle_imu: {exc}", file=sys.stderr)
+            return
+
+        if pkt.data_type == 1:  # Cartesian High
+            _Arr = _CartesianHighPoint * n
+            points = _C.cast(pkt.data, POINTER(_Arr)).contents
+            arr = np.ctypeslib.as_array(points)
+            xyz = np.stack((arr["x"], arr["y"], arr["z"]), axis=1).astype(np.float32) / 1000.0
+        elif pkt.data_type == 2:  # Cartesian Low (int16, cm)
+            class _LowPoint(_C.Structure):
+                _fields_ = [
+                    ("x", _C.c_int16),
+                    ("y", _C.c_int16),
+                    ("z", _C.c_int16),
+                    ("reflectivity", c_uint8),
+                    ("tag", c_uint8),
+                ]
+
+            _ArrL = _LowPoint * n
+            pts = _C.cast(pkt.data, POINTER(_ArrL)).contents
+            arr = np.ctypeslib.as_array(pts)
+            xyz = np.stack((arr["x"], arr["y"], arr["z"]), axis=1).astype(np.float32) / 100.0
+        else:
+            return
+
+        # Per Unitree G1 lidar guide: tags 1..15 mark low-confidence / noise points
+        # (range or intensity flag bits set). Keep tag == 0 (clean) or tag >= 16
+        # (multi-return etc.). Drops dust, drag points, glass interferers.
+        if self._tag_filter:
+            tag = np.asarray(arr["tag"])
+            mask = (tag == 0) | (tag >= 16)
+            if not mask.all():
+                xyz = xyz[mask]
+                if len(xyz) == 0:
+                    return
+
+        # --------------------------------------------------------------
+        # Aggregate packets belonging to the same "frame" (full 360°)
+        # --------------------------------------------------------------
+        # Each UDP packet contains only a tiny slice of a full scan – for the
+        # MID-360 that's merely 96 points. Feeding such sparse subsets into a
+        # SLAM backend like KISS-ICP is ineffective and typically produces an
+        # empty map. The packet header provides a monotonically increasing
+        # `frame_cnt` field which we can use to group packets that belong to
+        # the same rotation. We buffer points until the counter changes, then
+        # emit the *previous* frame in one batch via ``handle_points``.
+        #
+        # A small dictionary maps <lidar handle> → current frame accumulator so
+        # that multi-lidar setups would still work (although untested).
+        # --------------------------------------------------------------
+
+        # ------------------------------------------------------------------
+        # Aggregate packets for ~1 full rotation (≈50 ms @ 20 Hz)
+        # Lock only protects buffer state; handle_points is called outside.
+        # ------------------------------------------------------------------
+        frame_xyz = None
+        elapsed = 0.0
+        with self._frame_lock:
+            buf, last_t = self._frame_state.get(handle, ([], time.time()))
+            buf.append(xyz)
+            now = time.time()
+            elapsed = now - last_t
+            # Heuristic flush conditions: either 0.2 s have passed (≈4 full scans
+            # at 20 Hz) *or* we already gathered ≥ 120 packets (~12 k points).
+            if elapsed >= self._frame_time or len(buf) >= self._frame_packets:
+                frame_xyz = np.concatenate(buf, axis=0)
+                self._frame_state[handle] = ([], now)
+            else:
+                self._frame_state[handle] = (buf, last_t)
+
+        if frame_xyz is not None:
+            self._frame_print_counter += 1
+            if self._debug and (self._frame_print_counter % self._print_every_n_frames == 0):
+                print(f"[Livox2] frame {frame_xyz.shape[0]} pts  (Δt={elapsed*1000:.1f} ms)")
+            try:
+                self.handle_points(frame_xyz)
+            except Exception as exc:
+                print("Exception in handle_points:", exc, file=sys.stderr)
+
+    # ------------------------------------------------------------------
+    def _on_info_change(self, handle: int, info_ptr, _client):
+        if self._debug:
+            print(f"[Livox2] InfoChange handle={handle}")
+
+        # Set work mode to NORMAL (1) to begin emitting points.
+        kNormal = 1
+        _lib.SetLivoxLidarWorkMode(handle, kNormal, None, None)
+
+        # Ensure point-cloud sending is enabled
+        _lib.EnableLivoxLidarPointSend(handle, None, None)
+
+        # Ensure data type is Cartesian High (1)
+        _lib.SetLivoxLidarPclDataType(handle, 1, None, None)
+
+
+if __name__ == "__main__":
+    cfg = Path("mid360_config.json")
+    if not cfg.exists():
+        # generate a bare-bones config for 192.168.123.222
+        host_ip = os.environ.get("HOST_IP", "192.168.123.222")
+        data = {
+            "MID360": {
+                "lidar_net_info": {
+                    "cmd_data_port": 56100,
+                    "push_msg_port": 56200,
+                    "point_data_port": 56300,
+                    "imu_data_port": 56400,
+                    "log_data_port": 56500,
+                },
+                "host_net_info": [
+                    {
+                        "host_ip": host_ip,
+                        "multicast_ip": "224.1.1.5",
+                        "cmd_data_port": 56101,
+                        "push_msg_port": 56201,
+                        "point_data_port": 56301,
+                        "imu_data_port": 56401,
+                        "log_data_port": 56501,
+                    }
+                ],
+            }
+        }
+        cfg.write_text(json.dumps(data, indent=2))
+        print("[Livox2] Wrote default mid360_config.json with host_ip", host_ip)
+
+    lidar = Livox2(cfg, host_ip="192.168.123.222")
+    lidar.spin()
diff --git a/Lidar/DataMap/SLAM_session_memory.json b/Lidar/DataMap/SLAM_session_memory.json
new file mode 100644
index 0000000..385ae79
--- /dev/null
+++ b/Lidar/DataMap/SLAM_session_memory.json
@@ -0,0 +1,36 @@
+{
+  "entries": {
+    "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Lidar/DataMap/Work.ply": {
+      "ref_map": "/home/zedx/Robotics_workspace/yslootahtech/G1_Lootah/Lidar/DataMap/Work.ply",
+      "timestamp": 1774532907.4036179,
+      "fitness": 0.9957550030321407,
+      "rmse": 0.23353287961998817,
+      "transform": [
+        [
+          0.45460289796821224,
+          -0.8848542833763506,
+          -0.1018287893939065,
+          -1.9883602903057822
+        ],
+        [
+          0.8887660711216834,
+          0.4581630305179823,
+          -0.013472501234199874,
+          -0.6910650879555422
+        ],
+        [
+          0.058575387167564615,
+          -0.08437733497275234,
+          0.9947107063669648,
+          -0.21780082820430277
+        ],
+        [
+          0.0,
+          0.0,
+          0.0,
+          1.0
+        ]
+      ]
+    }
+  }
+}
\ No newline at end of file
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new file mode 100644
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new file mode 100644
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