Accurate Mapping and Planning for Autonomous Racing
| Autor: | Martin R. Oswald, Abel Gawel, Niclas Vödisch, Adrian Brandemuehl, Victor Reijgwart, Jen Jen Chung, Benson Kuan, Lukas Schaupp, Mathias Bürki, Hermann Blum, Leiv Andresen, Lukas Bernreiter, Roland Siegwart, Alex Hönger |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Robotics 0209 industrial biotechnology 020901 industrial engineering & automation Computer science Pipeline (computing) Real-time computing 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing 02 engineering and technology Robotics (cs.RO) |
| Zdroj: | IROS |
| DOI: | 10.1109/iros45743.2020.9341702 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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