Implements real-time person detection + tracking pipeline for the
follow-me motion controller on Jetson Orin Nano Super (D435i).
Core components
- TargetTrack.msg: bearing_deg, distance_m, confidence, bbox, vel_bearing_dps,
vel_dist_mps, depth_quality (0-3)
- _person_tracker.py (pure-Python, no ROS2/runtime deps):
· 8-state constant-velocity Kalman filter [cx,cy,w,h,vcx,vcy,vw,vh]
· Greedy IoU data association
· HSV torso colour histogram re-ID (16H×8S, Bhattacharyya similarity)
with fixed saturation clamping (s = (cmax−cmin)/cmax, clipped to [0,1])
· FollowTargetSelector: nearest person auto-lock, hold_frames hysteresis
· TENTATIVE→ACTIVE after min_hits; LOST track removal after max_lost_frames
with per-frame lost_age increment across all LOST tracks
· bearing_from_pixel, depth_at_bbox (median, quality flags)
- person_tracking_node.py:
· YOLOv8n via ultralytics (TRT FP16 on first run) → HOG+SVM fallback
· Subscribes colour + depth + camera_info + follow_start/stop
· Publishes /saltybot/target_track at ≤30 fps
- test/test_person_tracker.py: 59/59 tests passing
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.