sl-perception
1fd935b87e
New package saltybot_aruco_detect — DICT_4X4_50 ArUco detection from
RealSense D435i RGB, pose estimation, PoseArray + dock target output.
aruco_math.py (pure Python, no ROS2): rot_mat_to_quat (Shepperd),
rvec_to_quat (Rodrigues + cv2 fallback), tvec_distance, tvec_yaw_rad,
MarkerPose dataclass with lazy-cached distance_m/yaw_rad/lateral_m/quat.
aruco_detect_node.py (ROS2 node 'aruco_detect'):
Subscribes: /camera/color/image_raw (30Hz BGR8) + /camera/color/camera_info.
Converts to greyscale, cv2.aruco.ArucoDetector.detectMarkers().
estimatePoseSingleMarkers (legacy API) with solvePnP(IPPE_SQUARE) fallback.
Dock target: closest marker in dock_marker_ids (default=[42], empty=any),
filtered to max_dock_range_m (3.0m).
Publishes: /saltybot/aruco/markers (PoseArray — all detected, camera frame),
/saltybot/aruco/dock_target (PoseStamped — closest dock candidate,
position.z=forward, position.x=lateral), /saltybot/aruco/viz (MarkerArray
— SPHERE + TEXT per marker, dock in red), /saltybot/aruco/status (JSON
10Hz — detected_count, dock_distance_m, dock_yaw_deg, dock_lateral_m).
Optional debug image with drawDetectedMarkers + drawFrameAxes.
corner_refinement=CORNER_REFINE_SUBPIX.
config/aruco_detect_params.yaml, launch/aruco_detect.launch.py.
test/test_aruco_math.py: 22 unit tests (rotation/quat math, distance,
yaw sign/magnitude, MarkerPose accessors + caching).
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.