Students will develop and enhance the vision-based safety system for our remotely operated campus vehicle. The project focuses specifically on improving the ROS2 nodes that process camera and LiDAR data to detect pedestrians and obstacles. Students will work with existing sensor hardware to create more reliable detection algorithms, implement visualization tools for the operator dashboard, and thoroughly test the system's performance. This focused project is ideal for computer science or robotics students with ROS experience who want to apply their classroom knowledge of computer vision and sensor processing to a real-world application.

1. Enhanced ROS2 vision processing nodes that improve pedestrian and obstacle detection accuracy
2. Integration of camera and LiDAR data in a single ROS2 package with documented parameters
3. Visualization components for the operator dashboard to display detection results
4. Comprehensive test suite with pre-recorded sensor data to validate detection performance
5. Technical documentation including node architecture, message definitions, and parameter guides
6. Performance analysis comparing detection accuracy and processing efficiency before and after improvements