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 2019 AlphaPilot Challenge. 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 \({8}\,{\hbox {m}/\hbox {s}}\) and ranked second at the 2019 AlphaPilot Challenge.

本文提出了一种结合学习数据抽象、非线性过滤和时间最优轨迹规划的自主、基于视觉的无人机竞赛的新系统。该系统已成功部署在首届自主无人机竞速世界锦标赛：2019 AlphaPilot Challenge. 与仅检测下一个门的传统无人机竞赛系统相反，我们的方法利用任何可见的门并利用多个同时的门检测来补偿状态估计中的漂移并构建门的全局地图。全局地图和漂移补偿状态估计允许无人机在赛道上导航，即使大门不是立即可见，并进一步能够根据无人机的近似动态实时规划一条接近时间的最佳路径通过赛道。所提出的系统已被证明可以成功引导无人机通过紧张的比赛路线，速度达到\({8}\,{\hbox {m}/\hbox {s}}\)，并在2019 年 AlphaPilot 挑战赛中排名第二。