AirSim Drone Racing Lab

Ratnesh Madaan, Nicholas Gyde, Sai Vemprala, Matthew Brown, Keiko Nagami, Tim Taubner, Eric Cristofalo, Davide Scaramuzza, Mac Schwager, Ashish Kapoor
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:177-191, 2020.

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 {https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v123-madaan20a, title = {AirSim Drone Racing Lab}, 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}, booktitle = {Proceedings of the NeurIPS 2019 Competition and Demonstration Track}, pages = {177--191}, year = {2020}, editor = {Escalante, Hugo Jair and Hadsell, Raia}, volume = {123}, series = {Proceedings of Machine Learning Research}, month = {08--14 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v123/madaan20a/madaan20a.pdf}, url = {https://proceedings.mlr.press/v123/madaan20a.html}, 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 {https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing}. } }
Endnote
%0 Conference Paper %T AirSim Drone Racing Lab %A Ratnesh Madaan %A Nicholas Gyde %A Sai Vemprala %A Matthew Brown %A Keiko Nagami %A Tim Taubner %A Eric Cristofalo %A Davide Scaramuzza %A Mac Schwager %A Ashish Kapoor %B Proceedings of the NeurIPS 2019 Competition and Demonstration Track %C Proceedings of Machine Learning Research %D 2020 %E Hugo Jair Escalante %E Raia Hadsell %F pmlr-v123-madaan20a %I PMLR %P 177--191 %U https://proceedings.mlr.press/v123/madaan20a.html %V 123 %X 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 {https://github.com/microsoft/AirSim-NeurIPS2019-Drone-Racing}.
APA
Madaan, R., Gyde, N., Vemprala, S., Brown, M., Nagami, K., Taubner, T., Cristofalo, E., Scaramuzza, D., Schwager, M. & Kapoor, A.. (2020). AirSim Drone Racing Lab. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, in Proceedings of Machine Learning Research 123:177-191 Available from https://proceedings.mlr.press/v123/madaan20a.html.

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