History

sl-perception 2a9b03dd76 feat(perception): depth-based obstacle size estimator (Issue #348 )

Projects LIDAR clusters into the D435i depth image to estimate 3-D
obstacle width and height in metres.

- saltybot_scene_msgs/msg/ObstacleSize.msg      — new message
- saltybot_scene_msgs/msg/ObstacleSizeArray.msg — array wrapper
- saltybot_scene_msgs/CMakeLists.txt            — register new msgs
- saltybot_bringup/_obstacle_size.py            — pure-Python helper:
    CameraParams (intrinsics + LIDAR→camera extrinsics)
    ObstacleSizeEstimate (NamedTuple)
    lidar_to_camera()         LIDAR frame → camera frame transform
    project_to_pixel()        pinhole projection + bounds check
    sample_depth_median()     uint16 depth image window → median metres
    estimate_height()         vertical strip scan for row extent → height_m
    estimate_cluster_size()   full pipeline: cluster → size estimate
- saltybot_bringup/obstacle_size_node.py        — ROS2 node
    sub: /scan, /camera/depth/image_rect_raw, /camera/depth/camera_info
    pub: /saltybot/obstacle_sizes (ObstacleSizeArray)
    width from LIDAR bbox; height from depth strip back-projection;
    graceful fallback (LIDAR-only) when depth image unavailable;
    intrinsics latched from CameraInfo on first arrival
- test/test_obstacle_size.py                    — 33 tests, 33 passing
- setup.py                                      — add obstacle_size entry

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-03-03 13:32:41 -05:00

calibration

feat(calibration): IMX219 intrinsic + extrinsic calibration workflow (Issue #106 )

2026-03-02 08:38:24 -05:00

config

feat(mapping): RTAB-Map persistence + multi-session + map management (Issue #123 )

2026-03-02 09:17:54 -05:00

docs

feat: Orin Nano Super platform update + 4x IMX219 CSI cameras

2026-02-28 22:59:13 -05:00

models

feat(social): Orin dev environment — JetPack 6 + TRT conversion + systemd (#88 )

2026-03-02 08:08:57 -05:00

ros2_ws/src

feat(perception): depth-based obstacle size estimator (Issue #348 )

2026-03-03 13:32:41 -05:00

scripts

feat(social): Orin dev environment — JetPack 6 + TRT conversion + systemd (#88 )

2026-03-02 08:08:57 -05:00

systemd

feat(social): Orin dev environment — JetPack 6 + TRT conversion + systemd (#88 )

2026-03-02 08:08:57 -05:00

docker-compose.yml

feat(social): Orin dev environment — JetPack 6 + TRT conversion + systemd (#88 )

2026-03-02 08:08:57 -05:00

Dockerfile

feat: update SLAM stack for Jetson Orin Nano Super (67 TOPS, JetPack 6)

2026-02-28 21:46:27 -05:00

Dockerfile.social

feat(social): Orin dev environment — JetPack 6 + TRT conversion + systemd (#88 )

2026-03-02 08:08:57 -05:00

README.md

feat: Jetson Nano platform setup and Docker env (bd-1hcg)

2026-02-28 12:46:14 -05:00

README.md

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 peripherals
docs/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.