sl-perception
2f4540f1d3
Implements real-time moving obstacle detection, Kalman tracking, trajectory
prediction, and Nav2 costmap integration at 10 Hz / <50ms latency:
saltybot_dynamic_obs_msgs (ament_cmake):
• TrackedObject.msg — id, PoseWithCovariance, velocity, predicted_path,
predicted_times, speed, confidence, age, hits
• MovingObjectArray.msg — TrackedObject[], active_count, tentative_count,
detector_latency_ms
saltybot_dynamic_obstacles (ament_python):
• object_detector.py — LIDAR background subtraction (EMA occupancy grid),
foreground dilation + scipy connected-component
clustering → Detection list
• kalman_tracker.py — CV Kalman filter, state [px,py,vx,vy], Joseph-form
covariance update, predict_horizon() (non-mutating)
• tracker_manager.py — up to 20 tracks, Hungarian assignment
(scipy.optimize.linear_sum_assignment), TENTATIVE→
CONFIRMED lifecycle, miss-prune
• dynamic_obs_node.py — 10 Hz timer: detect→track→publish
/saltybot/moving_objects + MarkerArray viz
• costmap_layer_node.py — predicted paths → PointCloud2 inflation smear
→ /saltybot/dynamic_obs_cloud for Nav2 ObstacleLayer
• launch/dynamic_obstacles.launch.py + config/dynamic_obstacles_params.yaml
• test/test_dynamic_obstacles.py — 27 unit tests (27/27 pass, no ROS2 needed)
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.