自律走行レースカーの設計：知覚・状態推定・システム統合
Design of an Autonomous Racecar: Perception, State Estimation and System Integration
Formula Student Driverless競技で優勝した自律レースカー「flüela driverless」の設計を紹介し、モジュール式冗長サブシステム、EKF状態推定、LiDAR知覚、パーティクルフィルタSLAMの統合と実車実験での有効性を示した論文。
著者: Miguel de la Iglesia Valls, Hubertus Franciscus Cornelis Hendrikx, Victor Reijgwart, Fabio Vito Meier, Inkyu Sa, Renaud Dubé, Abel Roman Gawel, Mathias Bürki, Roland Siegwart
分類: cs.RO
原文アブストラクト
This paper introduces fl\"uela 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 fl\"uela'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.
