自動運転レースカーの高精度マッピングと計画
Accurate Mapping and Planning for Autonomous Racing
カメラとLiDARの融合、階層的マッピング、ベイズフィルタによる計画を組み合わせ、未知のコースで高速走行しながら高精度な地図を作成する自動運転レースカーのパイプラインを開発した。
著者: Leiv Andresen, Adrian Brandemuehl, Alex Hönger, Benson Kuan, Niclas Vödisch, Hermann Blum, Victor Reijgwart, Lukas Bernreiter, Lukas Schaupp, Jen Jen Chung, Mathias Bürki, Martin R. Oswald, Roland Siegwart, Abel Gawel
分類: cs.RO
原文アブストラクト
This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3~m/s to 12~m/s while still mapping with an acceptable RMSE of 0.29~m.
