Autonomous navigation for large Unmanned Aerial\u00a0Vehicles (UAVs) is fairly straight-forward, as expensive sensors\u00a0and monitoring devices can be employed. In contrast, obstacle\u00a0avoidance remains a challenging task for Micro Aerial Vehicles\u00a0(MAVs) which operate at low altitude in cluttered environments.\u00a0Unlike large vehicles, MAVs can only carry very light sensors,\u00a0such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a\u00a0system that navigates a small quadrotor helicopter autonomously\u00a0at low altitude through natural forest environments. Using only\u00a0a single cheap camera to perceive the environment, we are able\u00a0to maintain a constant velocity of up to 1.5m\/s. Given a small\u00a0set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can\u00a0avoid trees by adapting the MAVs heading. We demonstrate the\u00a0performance of our system in a more controlled environment\u00a0indoors, and in real natural forest environments outdoors.<\/p>\n","protected":false},"excerpt":{"rendered":"
Autonomous navigation for large Unmanned Aerial\u00a0Vehicles (UAVs) is fairly straight-forward, as expensive sensors\u00a0and monitoring devices can be employed. In contrast, obstacle\u00a0avoidance remains a challenging task for Micro Aerial Vehicles\u00a0(MAVs) which operate at low altitude in cluttered environments.\u00a0Unlike large vehicles, MAVs can only carry very light sensors,\u00a0such as cameras, making autonomous navigation through obstacles much more […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[],"msr_publishername":"International Conference on Robotics and 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