sl-perception
eb61207532
Adds ObstacleVelocity/ObstacleVelocityArray msgs and an
ObstacleVelocityNode that clusters /scan points, tracks each centroid
with a constant-velocity Kalman filter, and publishes velocity vectors
on /saltybot/obstacle_velocities.
New messages (saltybot_scene_msgs):
msg/ObstacleVelocity.msg — obstacle_id, centroid, velocity,
speed_mps, width_m, depth_m,
point_count, confidence, is_static
msg/ObstacleVelocityArray.msg — array wrapper with header
New files (saltybot_bringup):
saltybot_bringup/_obstacle_velocity.py — pure helpers (no ROS2 deps)
KalmanTrack constant-velocity 2-D KF: predict(dt) / update(centroid)
coasting counter → alive flag; confidence = age/n_init
associate() greedy nearest-centroid matching (O(N·M), strict <)
ObstacleTracker predict-all → associate → update/spawn → prune cycle
saltybot_bringup/obstacle_velocity_node.py
Subscribes /scan (BEST_EFFORT); reuses _lidar_clustering helpers;
publishes ObstacleVelocityArray on /saltybot/obstacle_velocities
Parameters: distance_threshold_m=0.20, min_points=3, range 0.05–12m,
max_association_dist_m=0.50, max_coasting_frames=5,
n_init_frames=3, q_pos=0.05, q_vel=0.50, r_pos=0.10,
static_speed_threshold=0.10
test/test_obstacle_velocity.py — 48 tests, all passing
Modified:
saltybot_scene_msgs/CMakeLists.txt — register new msgs
saltybot_bringup/setup.py — add obstacle_velocity 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.