Hyperstereo algorithms for the perception of terrain drop-offs

Abstract

The timely detection of terrain drop-offs is critical for safe and efficient off-road mobility, whether with human drivers or with terrain navigation systems that use autonomous machine-vision. In this paper, we propose a joint tracking and detection machine-vision approach for accurate and efficient terrain drop-off detection and localization. We formulate the problem using a hyperstereo camera system and build an elevation map using the range map obtained from a stereo algorithm. A terrain drop-off is then detected with the use of optimal drop-off detection filters applied to the range map. For more robust results, a method based on multi-frame fusion of terrain drop-off evidence is proposed. Also presented is a fast, direct method that does not employ stereo disparity mapping. We compared our algorithm's detection of terrain drop-offs with time-code data from human observers viewing the same video clips in stereoscopic 3D. The algorithm detected terrain drop-offs an average of 9 seconds sooner, or 12m farther, than the human observers. This suggests that passive image-based hyperstereo machine-vision may be useful as an early warning system for off-road mobility.

Correcting hyperstereopsis in a helmet-mounted vision system

“…Furthermore, the idea to combine modern image processing capabilities with hyperstereo sensors has been investigated in order to perceive terrain drop-offs. 9 Other authors try to use the special characteristics of hyperstereo setups to enhance the pilot's view, e.g., by enhanced depth perception. 10,11 In this paper we will develop methods to reduce or eliminate hyperstereopsis.…”