A Deep-learning-aided Automatic Vision-based Control Approach for Autonomous Drone Racing in Game of Drones Competition

Donghwi Kim, Hyunjee Ryu, Jedsadakorn Yonchorhor, David Hyunchul Shim
Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:37-46, 2020.

Abstract

In Game of Drones - Competition at NeurIPS 2019, this autonomous drone racing requires the drone to maneuver through the series of the gates without crashing. To complete the track, the drone has to be able to perceive the gates in the challenging environment from the FPV image in real-time and adjust its attitude accordingly. By utilizing deep-learning-aided detection and vision-based control approach, Team USRG completed the tier 2 challenge track passing the whole 21 gates in 81.19 seconds, and complete the tier 3 challenge track passing the whole 22 gates in 110.73 seconds.

Cite this Paper

BibTeX
@InProceedings{pmlr-v123-kim20b, title = {A Deep-learning-aided Automatic Vision-based Control Approach for Autonomous Drone Racing in Game of Drones Competition}, author = {Kim, Donghwi and Ryu, Hyunjee and Yonchorhor, Jedsadakorn and Shim, David Hyunchul}, booktitle = {Proceedings of the NeurIPS 2019 Competition and Demonstration Track}, pages = {37--46}, 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/kim20b/kim20b.pdf}, url = {https://proceedings.mlr.press/v123/kim20b.html}, abstract = {In Game of Drones - Competition at NeurIPS 2019, this autonomous drone racing requires the drone to maneuver through the series of the gates without crashing. To complete the track, the drone has to be able to perceive the gates in the challenging environment from the FPV image in real-time and adjust its attitude accordingly. By utilizing deep-learning-aided detection and vision-based control approach, Team USRG completed the tier 2 challenge track passing the whole 21 gates in 81.19 seconds, and complete the tier 3 challenge track passing the whole 22 gates in 110.73 seconds.} }
Endnote
%0 Conference Paper %T A Deep-learning-aided Automatic Vision-based Control Approach for Autonomous Drone Racing in Game of Drones Competition %A Donghwi Kim %A Hyunjee Ryu %A Jedsadakorn Yonchorhor %A David Hyunchul Shim %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-kim20b %I PMLR %P 37--46 %U https://proceedings.mlr.press/v123/kim20b.html %V 123 %X In Game of Drones - Competition at NeurIPS 2019, this autonomous drone racing requires the drone to maneuver through the series of the gates without crashing. To complete the track, the drone has to be able to perceive the gates in the challenging environment from the FPV image in real-time and adjust its attitude accordingly. By utilizing deep-learning-aided detection and vision-based control approach, Team USRG completed the tier 2 challenge track passing the whole 21 gates in 81.19 seconds, and complete the tier 3 challenge track passing the whole 22 gates in 110.73 seconds.
APA
Kim, D., Ryu, H., Yonchorhor, J. & Shim, D.H.. (2020). A Deep-learning-aided Automatic Vision-based Control Approach for Autonomous Drone Racing in Game of Drones Competition. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, in Proceedings of Machine Learning Research 123:37-46 Available from https://proceedings.mlr.press/v123/kim20b.html.

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