AirSim Drone Racing Lab
@inproceedings{madaan_airsim_2020,
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}
}