  Automating the navigation of unmanned aerial vehicles (UAVs) in diverse
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 on-board 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.
