Cameras provide a rich source of information while being passive, cheap\u00a0and lightweight for small Unmanned Aerial Vehicles (UAVs). In this work we\u00a0present the first implementation of receding horizon control, which is widely used\u00a0in ground vehicles, with monocular vision as the only sensing mode for autonomous\u00a0UAV flight in dense clutter. Two key contributions make this possible: novel coupling of perception and control via relevant and diverse, multiple interpretations\u00a0of the scene around the robot, leveraging recent advances in machine learning to\u00a0showcase anytime budgeted cost-sensitive feature selection, and fast non-linear regression for monocular depth prediction. We empirically demonstrate the efficacy\u00a0of our novel pipeline via real world experiments of more than 2 kms through dense\u00a0trees with an off-the-shelf quadrotor. Moreover our pipeline is designed to combine\u00a0information from other modalities like stereo and lidar.<\/p>\n","protected":false},"excerpt":{"rendered":"
Cameras provide a rich source of information while being passive, cheap\u00a0and lightweight for small Unmanned Aerial Vehicles (UAVs). In this work we\u00a0present the first implementation of receding horizon control, which is widely used\u00a0in ground vehicles, with monocular vision as the only sensing mode for autonomous\u00a0UAV flight in dense clutter. 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