sl-jetson 50971c0946
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feat(social): facial expression recognition — TRT FP16 emotion CNN (Issue #161)
- Add Expression.msg / ExpressionArray.msg ROS2 message definitions
- Add emotion_classifier.py: 7-class CNN (happy/sad/angry/surprised/fearful/disgusted/neutral)
  via TensorRT FP16 with landmark-geometry fallback; EMA per-person smoothing; opt-out registry
- Add emotion_node.py: subscribes /social/faces/detections, runs TRT crop inference (<5ms),
  publishes /social/faces/expressions and /social/emotion/context JSON for LLM
- Wire emotion context into conversation_node.py: emotion hint injected into LLM prompt
  when speaker shows non-neutral affect; subscribes /social/emotion/context
- Add emotion_params.yaml config and emotion.launch.py launch file
- Add 67-test suite (test_emotion_classifier.py): classifier, tracker, opt-out, heuristic

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-02 10:40:54 -05:00
..

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

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.