Image-Based Visual Perception and Representation for Collision Avoidance
We present a novel on-board perception system for collision avoidance by micro air vehicles (MAV). An egocentric cylindrical representation is utilized to model the world using forward-looking stereo vision. This efficient representation enables a 360° field of regard, as the vehicle moves around and disparity maps are fused temporally on the cylindrical map. For this purpose, we developed a new Gaussian Mixture Models-based disparity image fusion algorithm, with an extension to handle independently moving objects (IMO). The extension improves scene models in case of moving objects, where standard temporal fusion approaches cannot detect movers and introduce errors in world models due to the common static scene assumption. The on-board implementation of the vision pipeline provides disparity maps on a 360o egocentric cylindrical surface at 10 Hz. The perception output is used in our system by real-time motion planning with collision avoidance on the MAV.