论文标题
Alphapilot：自动无人机赛车
AlphaPilot: Autonomous Drone Racing
论文作者
论文摘要
本文介绍了一种新型系统，用于将基于视觉的无人机赛车结合在一起，结合了学习的数据抽象，非线性滤波和时间最佳的轨迹计划。该系统已成功部署在第一个自主无人机赛车世界冠军赛：2019年Alphapilot挑战赛。与仅检测下一个门的传统无人机赛车系统相反，我们的方法利用了任何可见的门，并利用多个同时的门检测来补偿州估计中的漂移并建立全局的大门地图。全球地图和浮动的状态估算使无人机即使不立即可见大门，也可以通过近似无人机动力学实时计划近距离的赛车路线。已提出的系统已被证明可以成功地指导无人机通过紧密的比赛课程达到800万/s的速度，并在2019年Alphapilot挑战赛中排名第二。
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 8m/s and ranked second at the 2019 AlphaPilot Challenge.
