Vision and learning for deliberative monocular cluttered flight

Abstract

Cameras provide a rich source of information while being passive, cheap and lightweight for small Unmanned AerialVehicles (UAVs). In thisworkwe present the first implementation of receding horizon control, which is widely used in ground vehicles, with monocular vision as the only sensing mode for autonomous UAV flight in dense clutter. Two key contributions make this possible: novel coupling of perception and control via relevant and diverse, multiple interpretations of the scene around the robot, leveraging recent advances in machine learning to showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy of our novel.