Multi-Modal Social Human Navigation Dataset - A robotics research platform developed at George Mason University for collecting data on human-robot interactions in social navigation contexts.
Project Website: https://cs.gmu.edu/~xiao/Research/MuSoHu/
This project collects social human navigation data in natural human-inhabited public spaces. An egocentric data collection sensor suite worn by walking humans captures multi-modal robot perception data.
- ~100 km of navigation data
- 20 hours of recording
- 300 trials across various environments
- 13 human participants
- Multiple public spaces with natural social navigation interactions
The wearable data collection device includes:
- 3D LiDAR - Robosense Helios 32 (360° environmental scanning)
- Stereo & Depth Camera - Stereolabs ZED 2i (visual perception and depth sensing)
- IMU - Inertial Measurement Unit (motion tracking)
- Odometry/Actions - Online processing for human navigation behavior extraction
- Microphone Array - Seeed Studio ReSpeaker Mic Array (spatial audio capture)
For complete documentation, setup instructions, and production deployment guides, see:
- Production Web Service Setup - Complete guide for deploying the web application
- Production Setup Details - Detailed systemd configuration and monitoring
MuSoHu is a platform for collecting data for social navigation research. It is built using ROS2 and includes a web interface for system interaction.
- ROS2-based robotics platform
- Multi-sensor integration (ZED camera, LiDAR, IMU, Audio)
- Web interface for system control and monitoring
- Deployment with automatic restart capability
- Helmet-mounted sensor suite for data collection
helmet_bringup/- ROS2 launch files and sensor configurationsweb-app/- Flask web interface for system controlscripts/- Setup, installation, and utility scriptssetup/- Installation and configuration scriptsutils/- Helper utilities and loggingudev_rules/- Device access rules for sensorshotspot/- WiFi hotspot configuration
docs/- Complete documentation and guides