Abstract
This paper presents a novel system for autonomous,vision-based drone racing combining learned data abstraction,nonlinear filtering, and time-optimal trajectory planning. Thesystem has successfully been deployed at the first autonomousdrone racing world championship: the2019 AlphaPilot Challenge.Contrary to traditional drone racing systems, which only detectthe next gate, our approach makes use of any visible gate andtakes advantage of multiple, simultaneous gate detections tocompensate for drift in the state estimate and build a global mapof the gates. The global map and drift-compensated state estimateallow the drone to navigate through the race course even whenthe gates are not immediately visible and further enable to plana near time-optimal path through the race course in real timebased on approximate drone dynamics. The proposed system hasbeen demonstrated to successfully guide the drone through tightrace courses reaching speeds up to8 m/sand ranked second atthe2019 AlphaPilot Challenge
Foehn, Philipp