LiDAR-based Cone Extraction and Heuristic Trajectory Optimization for Autonomous Formula Student Racing

Addressing the challenges of autonomous racing in Formula Student competitions, this work proposes a novel LiDAR-based perception and trajectory optimization pipeline. Our key contributions include a robust cone detection algorithm for accurate track-edge identification from point clouds. These detections are integrated into a global map using an adapted FASTSLAM 2.0 framework, enhancing mapping accuracy in dynamic environments. Furthermore, we introduce a heuristic trajectory planner that minimizes path length and curvature trade-offs via cubic splines and a BFGS-based solver, and crucially, an offline regression model for real-time optimal weight estimation. The complete system is validated on real-world tracks with RTK-based ground truth, demonstrating superior mapping precision and efficient trajectory generation for high-performance autonomous navigation.