Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

Matthias Muller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

Abstract


Automating the navigation of unmanned aerial vehicles (UAVs) indiverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient onboard processing critical for real-world deployment. From a broader perspective, our results underline the importance of extensive data augmentation techniques to improve robustness in end-to-end learning setups.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Muller_2018_ECCV_Workshops,
author = {Muller, Matthias and Casser, Vincent and Smith, Neil and Michels, Dominik L. and Ghanem, Bernard},
title = {Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
month = {September},
year = {2018}
}