ROS, RTABMAP & Detectron2 Racecar

This project explores approaches to autonomous race car navigation using ROS, Detectron2's object detection and image segmentation capabilities for localization, object detection and avoidance, and RTABMAP for mapping. You can specify performance metrics, train several models on Detectron2, and retrieve the best performer to run inference on a Jetson module. This work addresses camera-based challenges such as lighting issues and less visual information for mapping and navigation. The work is part of the 2020-2021 Data Science Capstone sequence with Triton AI at UCSD.