AlphaPilot：ビジョンベース自律ドローン競技システム
AlphaPilot: Autonomous Drone Racing
複数のゲートを同時検出してドリフト補正した状態推定とグローバルマップを構築し、近時間最適軌道計画でドローンを自律飛行させるシステムを開発、2019年AlphaPilot Challengeで2位を獲得した。
著者: Philipp Foehn, Dario Brescianini, Elia Kaufmann, Titus Cieslewski, Mathias Gehrig, Manasi Muglikar, Davide Scaramuzza
分類: cs.RO, cs.CV, cs.SY, eess.SY
原文アブストラクト
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.
