sl-perception
a9717cd602
New packages:
saltybot_scene_msgs — 4 msgs (SceneObject, SceneObjectArray, RoomClassification, BehaviorHint)
saltybot_scene — 3 nodes + launch + config + TRT build script
Nodes:
scene_detector_node — YOLOv8-nano TRT FP16 (target ≥15 FPS @ 640×640);
synchronized RGB+depth input; filters scene classes
(chairs, tables, doors, stairs, pets, appliances);
3D back-projection via aligned depth; depth-based hazard
scan (HazardClassifier); room classification at 2Hz;
publishes /social/scene/objects + /social/scene/hazards
+ /social/scene/room_type
behavior_adapter_node — adapts speed_limit_mps + personality_mode from room
type and hazard severity; publishes BehaviorHint on
/social/scene/behavior_hint (on-change + 1Hz heartbeat)
costmap_publisher_node — converts SceneObjectArray → PointCloud2 disc rings
for Nav2 obstacle_layer + MarkerArray for RViz;
publishes /social/scene/obstacle_cloud
Modules:
yolo_utils.py — YOLOv8 preprocess/postprocess (letterbox, cx/cy/w/h decode,
NMS), COCO+custom class table (door=80, stairs=81, wet=82),
hazard-by-class mapping
room_classifier.py — rule-based (object co-occurrence weights + softmax) with
optional MobileNetV2 TRT/ONNX backend (Places365-style 8-class)
hazard_classifier.py — depth-only hazard patterns: drop (row-mean cliff), stairs
(alternating depth bands), wet floor (depth std-dev), glass
(zero depth + strong Sobel edges in RGB)
scripts/build_scene_trt.py — export YOLOv8n → ONNX → TRT FP16; optionally build
MobileNetV2 room classifier engine; includes benchmark
Topic map:
/social/scene/objects SceneObjectArray ~15+ FPS
/social/scene/room_type RoomClassification ~2 Hz
/social/scene/hazards SceneObjectArray on hazard
/social/scene/behavior_hint BehaviorHint on-change + 1 Hz
/social/scene/obstacle_cloud PointCloud2 Nav2 obstacle_layer
/social/scene/object_markers MarkerArray RViz debug
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.