2018-10-23-kaufmann18a.md

title	abstract	keywords	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Deep Drone Racing: Learning Agile Flight in Dynamic Environments	Autonomous agile flight brings up fundamental challenges in robotics, such as coping with unreliable state estimation, reacting optimally to dynamically changing environments, and coupling perception and action in real time under severe resource constraints. In this paper, we consider these challenges in the context of autonomous, vision-based drone racing in dynamic environments. Our approach combines a convolutional neural network (CNN) with a state-of-the-art path-planning and control system. The CNN directly maps raw images into a robust representation in the form of a waypoint and desired speed. This information is then used by the planner to generate a short, minimum-jerk trajectory segment and corresponding motor commands to reach the desired goal. We demonstrate our method in autonomous agile flight scenarios, in which a vision-based quadrotor traverses drone-racing tracks with possibly moving gates. Our method does not require any explicit map of the environment and runs fully onboard. We extensively test the precision and robustness of the approach in simulation and in the physical world. We also evaluate our method against state-of-the-art navigation approaches and professional human drone pilots.	Drone Racing, Learning Agile Flight, Learning for Control.	inproceedings	Proceedings of Machine Learning Research	kaufmann18a	0	Deep Drone Racing: Learning Agile Flight in Dynamic Environments	133	145	133-145	133	false	Kaufmann, Elia and Loquercio, Antonio and Ranftl, Rene and Dosovitskiy, Alexey and Koltun, Vladlen and Scaramuzza, Davide	given family Elia Kaufmann given family Antonio Loquercio given family Rene Ranftl given family Alexey Dosovitskiy given family Vladlen Koltun given family Davide Scaramuzza	2018-10-23		PMLR	Proceedings of The 2nd Conference on Robot Learning	87	inproceedings	date-parts 2018 10 23	http://proceedings.mlr.press/v87/kaufmann18a/kaufmann18a.pdf	label link Supplementary video https://www.youtube.com/watch?v=8RILnqPxo1s