sl-perception
4dbb4c6f0d
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Adds PersonTrack/PersonTrackArray msgs and a PersonReidNode that matches
individuals across camera views using HSV colour histogram appearance
features and cosine similarity, with EMA gallery update and 30s stale timeout.
New messages (saltybot_scene_msgs):
msg/PersonTrack.msg — track_id, camera_id, bbox, confidence,
first_seen, last_seen, is_stale
msg/PersonTrackArray.msg — array wrapper with header
New files (saltybot_bringup):
saltybot_bringup/_person_reid.py — pure kinematics (no ROS2 deps)
extract_hsv_histogram() 2-D HS histogram (H=16, S=8 → 128-dim, L2-norm)
cosine_similarity() handles zero/non-unit vectors
match_track() best gallery match above threshold (strict >)
TrackGallery add/update/match/mark_stale/prune_stale
TrackEntry mutable dataclass; EMA feature blend (α=0.3)
saltybot_bringup/person_reid_node.py
Subscribes /camera/color/image_raw + /saltybot/scene/objects (BEST_EFFORT)
Crops COCO person (class_id=0) ROIs; extracts features; matches gallery
Publishes PersonTrackArray on /saltybot/person_tracks at 5 Hz
Parameters: camera_id, similarity_threshold=0.75, stale_timeout_s=30,
max_tracks=20, publish_hz=5.0
test/test_person_reid.py — 50 tests, all passing
Modified:
saltybot_scene_msgs/CMakeLists.txt — register PersonTrack/Array msgs
saltybot_bringup/setup.py — add person_reid console_script
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Jetson Nano — AI/SLAM Platform Setup
Self-balancing robot: Jetson Nano dev environment for ROS2 Humble + SLAM stack.
Stack
| Component | Version / Part |
|---|---|
| Platform | Jetson Nano 4GB |
| JetPack | 4.6 (L4T R32.6.1, CUDA 10.2) |
| ROS2 | Humble Hawksbill |
| DDS | CycloneDDS |
| SLAM | slam_toolbox |
| Nav | Nav2 |
| Depth camera | Intel RealSense D435i |
| LiDAR | RPLIDAR A1M8 |
| MCU bridge | STM32F722 (USB CDC @ 921600) |
Quick Start
# 1. Host setup (once, on fresh JetPack 4.6)
sudo bash scripts/setup-jetson.sh
# 2. Build Docker image
bash scripts/build-and-run.sh build
# 3. Start full stack
bash scripts/build-and-run.sh up
# 4. Open ROS2 shell
bash scripts/build-and-run.sh shell
Docs
docs/pinout.md— GPIO/I2C/UART pinout for all peripheralsdocs/power-budget.md— 10W power envelope analysis
Files
jetson/
├── Dockerfile # L4T base + ROS2 Humble + SLAM packages
├── docker-compose.yml # Multi-service stack (ROS2, RPLIDAR, D435i, STM32)
├── README.md # This file
├── docs/
│ ├── pinout.md # GPIO/I2C/UART pinout reference
│ └── power-budget.md # Power budget analysis (10W envelope)
└── scripts/
├── entrypoint.sh # Docker container entrypoint
├── setup-jetson.sh # Host setup (udev, Docker, nvpmodel)
└── build-and-run.sh # Build/run helper
Power Budget (Summary)
| Scenario | Total |
|---|---|
| Idle | 2.9W |
| Nominal (SLAM active) | ~10.2W |
| Peak | 15.4W |
Target: 10W (MAXN nvpmodel). Use RPLIDAR standby + 640p D435i for compliance.
See docs/power-budget.md for full analysis.