History

sl-perception b722279739 feat(bringup): scan height filter with IMU pitch compensation (Issue #211 )

Two files added to saltybot_bringup:
- _scan_height_filter.py: pure-Python helpers (no rclpy) —
  filter_scan_by_height() projects each LIDAR ray to world-frame height
  using pitch/roll from the IMU and filters ground/ceiling returns;
  pitch_roll_from_accel() uses convention-agnostic atan2 formula;
  AttitudeEstimator low-pass filters the accelerometer attitude.
- scan_height_filter_node.py: subscribes /scan + /camera/imu, publishes
  /scan_filtered (LaserScan) for Nav2 at source rate (up to 20 Hz).

setup.py: adds scan_height_filter entry point.
18/18 unit tests pass.

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

2026-03-02 11:50:56 -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(bringup): scan height filter with IMU pitch compensation (Issue #211 )

2026-03-02 11:50:56 -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.