AirSim Drone Racing Lab

  series = {Proceedings of {Machine} {Learning} {Research}},
  title = {{AirSim} {Drone} {Racing} {Lab}},
  volume = {123},
  abstract = {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},
  booktitle = {Proceedings of the {NeurIPS} 2019 {Competition} and {Demonstration} {Track}},
  publisher = {PMLR},
  author = {Madaan, Ratnesh and Gyde, Nicholas and Vemprala, Sai and Brown, Matthew and Nagami, Keiko and Taubner, Tim and Cristofalo, Eric and Scaramuzza, Davide and Schwager, Mac and Kapoor, Ashish},
  month = dec,
  year = {2020},
  keywords = {game\_theoretic\_planning},
  pages = {177--191},
  month_numeric = {12}