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Type
PreprintKAUST Department
Visual Computing Center (VCC)Date
2017-08-19Permanent link to this record
http://hdl.handle.net/10754/626557.1
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Show full item recordAbstract
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in the recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of data for training. In this paper, we develop a photo-realistic simulator that can afford the generation of large amounts of training data (both images rendered from the UAV camera and its controls) to teach a UAV to autonomously race through challenging tracks. We train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing. Training is done through imitation learning enabled by 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.Publisher
arXivarXiv
arXiv:1708.05884Additional Links
http://arxiv.org/abs/1708.05884v2http://arxiv.org/pdf/1708.05884v2