Foehn Philipp, Brescianini Dario, Kaufmann Elia, Cieslewski Titus, Gehrig Mathias, Muglikar Manasi, Scaramuzza Davide
Dep. of Informatics, Dep. of Neuroinformatics, University of Zurich and ETH, Zurich, Switzerland.
Auton Robots. 2022;46(1):307-320. doi: 10.1007/s10514-021-10011-y. Epub 2021 Oct 19.
This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning. The system has successfully been deployed at the first autonomous drone racing world championship: the . Contrary to traditional drone racing systems, which only detect the next gate, our approach makes use of any visible gate and takes advantage of multiple, simultaneous gate detections to compensate for drift in the state estimate and build a global map of the gates. The global map and drift-compensated state estimate allow the drone to navigate through the race course even when the gates are not immediately visible and further enable to plan a near time-optimal path through the race course in real time based on approximate drone dynamics. The proposed system has been demonstrated to successfully guide the drone through tight race courses reaching speeds up to and ranked second at the .
本文提出了一种用于基于视觉的自主无人机竞赛的新型系统，该系统结合了学习到的数据抽象、非线性滤波和时间最优轨迹规划。该系统已成功部署在首届自主无人机竞赛世界锦标赛上。与传统的无人机竞赛系统不同，传统系统仅检测下一个门，而我们的方法利用任何可见的门，并利用多个同时进行的门检测来补偿状态估计中的漂移，并构建门的全局地图。全局地图和经过漂移补偿的状态估计使无人机即使在门不立即可见时也能在赛道上导航，并进一步能够基于近似的无人机动力学实时规划出接近时间最优的赛道路径。所提出的系统已被证明能够成功地引导无人机通过狭窄的赛道，速度高达 ，并在 中获得第二名。
