AirSimドローンレースラボ：自律飛行アルゴリズム開発のためのシミュレーション基盤
AirSim Drone Racing Lab
ドローンレース向けに、フォトリアルな環境でコース生成や複数センサー・カメラモデルを備え、計画・制御・視覚・学習アルゴリズムをベンチマークできるシミュレーションフレームワークを提案し、NeurIPS 2019の競技会で実証した。
著者: Ratnesh Madaan, Nicholas Gyde, Sai Vemprala, Matthew Brown, Keiko Nagami, Tim Taubner, Eric Cristofalo, Davide Scaramuzza, Mac Schwager, Ashish Kapoor
分類: cs.RO, cs.CV
原文アブストラクト
Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control. We introduce AirSim Drone Racing Lab, a simulation framework for enabling fast prototyping of algorithms for autonomy and enabling machine learning research in this domain, with the goal of reducing the time, money, and risks associated with field robotics. Our framework enables generation of racing tracks in multiple photo-realistic environments, orchestration of drone races, comes with a suite of gate assets, allows for multiple sensor modalities (monocular, depth, neuromorphic events, optical flow), different camera models, and benchmarking of planning, control, computer vision, and learning-based algorithms. We used our framework to host a simulation based drone racing competition at NeurIPS 2019. The competition binaries are available at our github repository.
