Stereo based obstacle detection for an unmanned air vehicle

This paper presents the visual threat awareness (VISTA) system for real time collision obstacle detection for an unmanned air vehicle (UAV). Computational stereo performance has progressed such that several commercial or open source implementations are available which operate at frame rate, but suffer from well known correspondence errors. We show that introducing a global segmentation step after commodity stereo can increase robustness and leverage existing stereo software. The global segmentation step is based on a graph structure appropriate for collision detection, human vision inspired foveation, perceptual organization and graph partitioning using the minimum s-t graph cut. This system has been prototyped using the Sarnoff Acadia I vision processor to enable processing of 640 times 480 resolution imagery at 5-10 Hz operation on embedded avionics. We describe system theory, demonstrate segmentation results on scenes of increasing complexity, and show flight experiment results on Georgia Tech's GT-Max autonomous helicopter against real collision obstacles