sl-perception
d8b25bad77
Replace single-motor vesc_odometry_bridge with dual-CAN differential drive odometry for left (CAN 61) and right (CAN 79) VESC motors. New files: - diff_drive_odom.py: pure-Python kinematics (eRPM→wheel vel, exact arc integration, heading wrap), no ROS deps, fully unit-tested - test/test_vesc_odometry.py: 20 unit tests (straight, arc, spin, invert_right, guard conditions) — all pass - config/vesc_odometry_params.yaml: configurable wheel_radius, wheel_separation, motor_poles, invert_right, covariance tuning Updated: - vesc_odometry_bridge.py: rewritten as VESCCANOdometryNode; subscribes to /vesc/can_61/state and /vesc/can_79/state (std_msgs/String JSON); publishes /odom and /saltybot/wheel_odom (nav_msgs/Odometry) + TF odom→base_link with proper 6×6 covariance matrices - odometry_bridge.launch.py: updated to launch vesc_can_odometry with vesc_odometry_params.yaml - setup.py: added vesc_can_odometry entry point + config install - pose_fusion_node.py: added optional wheel_odom_topic subscriber that feeds DiffDriveOdometry velocities into EKF via update_vo_velocity - pose_fusion_params.yaml: added use_wheel_odom, wheel_odom_topic, sigma_wheel_vel_m_s, sigma_wheel_omega_r_s parameters 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.