sl-perception
82ad626a94
New package saltybot_obstacle_detect — RANSAC ground plane fitting on
D435i depth images with 2D grid BFS obstacle clustering.
ground_plane.py (pure Python + numpy):
fit_ground_plane(pts, n_iter=50, inlier_thresh_m=0.06): RANSAC over 3D
point cloud in camera optical frame (+Z forward). Samples 3 points, fits
plane via cross-product, counts inliers, refines via SVD on best inlier
set. Orients normal toward -Y (upward in world). Returns (normal, d).
height_above_plane(pts, plane): signed h = d - n·p (h>0 = above ground).
obstacle_mask(pts, plane, min_h, max_h): min_obstacle_h_m < h < max_h.
ground_mask(pts, plane, thresh): inlier classification.
obstacle_clusterer.py (pure Python + numpy):
cluster_obstacles(pts, heights, cell_m=0.30, min_pts=5): projects
obstacle 3D points onto (X,Z) bird's-eye plane, discretises into grid
cells, runs 4-connected BFS flood-fill, returns ObstacleCluster list
sorted by forward distance. ObstacleCluster: centroid(3), radius_m,
height_m, n_pts + distance_m/lateral_m properties.
obstacle_detect_node.py (ROS2 node 'obstacle_detect'):
- Subscribes: /camera/depth/camera_info (latched, once),
/camera/depth/image_rect_raw (BEST_EFFORT, 30Hz float32 depth).
- Pipeline: stride downsample (default 8x → 80x60) → back-project to
3D → RANSAC ground plane (temporally blended α=0.3) → obstacle mask
(min_h=0.05m, max_h=0.80m) → BFS clustering → alert classification.
- Publishes:
/saltybot/obstacles (MarkerArray): SPHERE markers colour-coded
DANGER(red)/WARN(yellow)/CLEAR(green) + distance TEXT labels.
/saltybot/obstacles/cloud (PointCloud2): xyz float32 non-ground pts.
/saltybot/obstacles/alert (String JSON): alert_level, closest_m,
obstacle_count, per-obstacle {x,y,z,radius_m,height_m,level}.
- Safety zone integration (depth_estop_enabled=false by default):
DANGER → zero Twist to depth_estop_topic (/cmd_vel_input) feeds
into safety_zone's cmd_vel chain for independent depth e-stop.
config/obstacle_detect_params.yaml: all tuneable parameters with comments.
launch/obstacle_detect.launch.py: single node with params_file arg.
test/test_ground_plane.py: 10 unit tests (RANSAC correctness, normal
orientation, height computation, inlier/obstacle classification).
test/test_obstacle_clusterer.py: 8 unit tests (single/dual cluster,
distance sort, empty, min_pts filter, centroid accuracy, range clip).
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.