Description

This thesis develops a real-time and accurate LiDAR-Inertial Odometry SLAM algorithm for high-speed Formula Student race cars. It focuses on enhancing autonomous navigation through precise localization and mapping in static environments. The algorithm is optimized for fast processing and reliability to meet the performance demands of Formula Student competitions, bridging cutting-edge SLAM approaches with the challenges of autonomous racing.