Obstacle detection and navigation planning for autonomous micro aerial vehicles

Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance. We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.