Multimodal Obstacle Detection and Collision Avoidance for Micro Aerial Vehicles

Reliably perceiving obstacles and avoiding collisions is key for the fully autonomous application of micro aerial vehicles (MAVs). Limiting factors for increasing autonomy and complexity of MAVs are limited onboard sensing and limited onboard processing power. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception. We developed a lightweight 3D laser scanner and visual obstacle detection using wide-angle stereo cameras. Detected obstacles are aggregated in egocentric grid maps. We implemented a fast reactive collision avoidance approach for safe operation in the vicinity of structures like buildings or vegetation.