Design of an Autonomous Racecar: Perception, State Estimation and System Integration

• Published in: 2018 IEEE International Conference on Robotics and Automation (ICRA) pp. 2048 - 2055
• Main Authors: Valls, Miguel I., Hendrikx, Hubertus F.C., Reijgwart, Victor J.F., Meier, Fabio V., Sa, Inkyu, Dube, Renaud, Gawel, Abel, Burki, Mathias, Siegwart, Roland
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2018
• Subjects: Automobiles; Current measurement; Global Positioning System; Laser radar; Robot sensing systems; State estimation; Wheels
• ISSN: 2577-087X
• DOI: 10.1109/ICRA.2018.8462829

This paper introduces jlüela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of flüela's design are its modular redundant sub-systems that allow robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF-based state estimation, LiDAR-based perception, and particle filter-based SLAM. We perform an extensive experimental evaluation on real-world data, demonstrating the system's effectiveness by outperforming the next-best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.