Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering

This paper proposes a reactive obstacle avoidance approach based solely on image data from a monocular camera stream. By clustering and analyzing the optical flow, this approach is able to identify potential collisions with dynamic obstacles. Epipolar geometry is exploited to derive velocity commands that ensure a collision-free path for a highly maneuverable autonomous vehicle via a real-time optimizer. First, the underlying image processing and optimization principles are explained in detail, before simulation results show the general feasibility of the approach. Finally, real-world tests with the ROboMObil, the German Aerospace Center's robotic electric vehicle, are provided to demonstrate its applicability.