论文标题
自动赛车的准确映射和计划
Accurate Mapping and Planning for Autonomous Racing
论文作者
论文摘要
本文介绍了在自动赛车上实施的感知，映射和规划管道。它是由2019年AMZ Driver Team开发的，该团队为2019年Formula Student Dermany（FSG）2019年无人驾驶竞赛开发，在那里整体赢得了第一名。提出的解决方案结合了相机和激光雷达数据的早期融合，一种分层的映射方法以及一种使用贝叶斯过滤的计划方法，可以在未知赛道上实现高速驾驶，同时创建准确的地图。我们对我们团队以前的解决方案进行基准测试，该解决方案赢得了FSG 2018，并以相同的速度行驶时显示出提高的准确性。此外，新管道使在未知环境中可靠地提高最大驾驶速度从3〜m/s到12〜m/s成为可能，而仍可以0.29〜m的可接受映射映射。
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.
