Obstacle detection for unmanned ground vehicles: a progress report

To detect obstacles during off-road autonomous navigation, unmanned ground vehicles (UGV's) must sense terrain geometry and composition (terrain type) under day, night, and low-visibility conditions. To sense terrain geometry, we have developed a real-time stereo vision system that uses a Datacube MV-200 and a 68040 CPU board to produce 256/spl times/240-pixel range images in about 0.6 seconds/frame. To sense terrain type, we used the same computing hardware with red and near infrared imagery to classify 256/spl times/240-pixel frames into vegetation and non-vegetation regions at a rate of five to ten frames/second. This paper reviews the rationale behind the choice of these sensors, describes their recent evolution and on-going development, and summarizes their use in demonstrations of autonomous UGV navigation over the past five years.