Learning a controller fusion network by online trajectory filtering for vision-based UAV racing

Matthias Muller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Autonomous UAV racing has recently emerged as an interesting research problem. The dream is to beat humans in this new fast-paced sport. A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert. However, such a policy is limited by the expert it imitates and scaling to other environments and vehicle dynamics is difficult. One approach to overcome the drawbacks of an end-to-end policy is to train a network only on the perception task and handle control with a PID or MPC controller. However, a single controller must be extensively tuned and cannot usually cover the whole state space. In this paper, we propose learning an optimized controller using a DNN that fuses multiple controllers. The network learns a robust controller with online trajectory filtering, which suppresses noisy trajectories and imperfections of individual controllers. The result is a network that is able to learn a good fusion of filtered trajectories from different controllers leading to significant improvements in overall performance. We compare our trained network to controllers it has learned from, end-to-end baselines and human pilots in a realistic simulation; our network beats all baselines in extensive experiments and approaches the performance of a professional human pilot.
Original languageEnglish (US)
Title of host publication2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PublisherIEEE
Pages573-581
Number of pages9
ISBN (Print)9781728125060
DOIs
StatePublished - Apr 10 2020

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