Learning visual odometry for unmanned aerial vehicles
We describe a method to learn the flight dynamics of an unmanned aerial vehicle (UAV). This follows the recent trend to adopt a learning approach to Visual Odometry (VO). Our novelty is the inclusion of a module to compensate for the roll pitch angular motion of the UAV due to body vibration. This is a significant deviation from most existing works that are applied to land vehicles. We empirically verify our results on real flight data, showing that after compensating for angular vibration, the ego-motion of the UAV can be robustly estimated even by using simple regression tools. This enables the advantages of learning based VO to be within reach of the UAV community.