Autonomous Aerial Inspection Using Visual-Inertial Robust Localization and Mapping.

With recent technological breakthroughs bringing fully autonomous inspection using small Unmanned Aerial Vehicles (UAVs) closer to reality, the community of Robotics has actively been developing the real-time perception capabilities able to run onboard such constraint platforms. Despite good progress, realistic deployment of autonomous UAVs in GPS-denied environments is still rudimentary. In this work, we propose a novel system to generate a collision-free path towards a user-specified inspection direction for a small UAV using monocular-inertial sensing only and performing all computation onboard. Estimating both the previously unknown scene and the UAV’s trajectory on the fly, this system is evaluated on real experiments outdoors in the presence of wind and poorly structured environments. Our analysis reveals the shortcomings of using sparse feature maps for planning, highlighting the importance of robust dense scene estimation proposed here.