sl-perception
572e578069
New package: saltybot_visual_odom (13 files, ~900 lines)
Nodes:
visual_odom_node — D435i IR1 stereo VO at 30 Hz
CUDA: SparsePyrLKOpticalFlow + FastFeatureDetector (GPU)
CPU fallback: calcOpticalFlowPyrLK + goodFeaturesToTrack
Essential matrix (5-pt RANSAC) + depth-aided metric scale
forward-backward consistency check on tracked points
Publishes /saltybot/visual_odom (Odometry)
odom_fusion_node — 5-state EKF [px, py, θ, v, ω] (unicycle model)
Fuses: wheel odom (/saltybot/rover_odom or tank_odom)
+ visual odom (/saltybot/visual_odom)
Slip failover: /saltybot/terrain JSON → 10× wheel noise on slip
Loop closure: /rtabmap/odom jump > 0.3m → EKF soft-correct
TF: publishes odom → base_link at 30 Hz
Publishes /saltybot/odom_fused + /saltybot/visual_odom_status
Modules:
optical_flow_tracker.py — CUDA/CPU sparse LK tracker with re-detection,
forward-backward consistency, ROI masking
stereo_vo.py — Essential matrix decomposition, camera→base_link
frame rotation, depth median scale recovery,
loop closure soft-correct, accumulated SE(3) pose
kalman_odom_filter.py — 5-state EKF: predict (unicycle), update_wheel,
update_visual, update_rtabmap (absolute pose);
Joseph-form covariance for numerical stability
Tests:
test/test_kalman_odom.py — 8 unit tests for EKF + StereoVO (no ROS deps)
Topic/TF map:
/camera/infra1/image_rect_raw → visual_odom_node
/camera/depth/image_rect_raw → visual_odom_node
/saltybot/visual_odom ← visual_odom_node (30 Hz)
/saltybot/rover_odom → odom_fusion_node
/saltybot/terrain → odom_fusion_node (slip signal)
/rtabmap/odom → odom_fusion_node (loop closure)
/saltybot/odom_fused ← odom_fusion_node (30 Hz)
odom → base_link TF ← odom_fusion_node (30 Hz)
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.