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Abstract : |
'lb detect obstacles during oll:road autonomous navigation, unmanned ground vehicles (UGV's) must sense terrain geometry and composition (ie. terrain type) under day, hiS. let. and low-visibil/ty conditions. To sense terrain geometry, wc have developed a real-time stereo vision em that us a Datacube MV-200 and a 68040 CPU board to produce 256 x 240-pixelrange images in about 0.6 seconds/frame. To sense terrain type, we are using the same computing hardware with red and near infrared imagery to classify 256 X 240-pixel frames into v egetation and non-vegetation regions at a rate of five to ten frames/second. This paper reviews the rationale behind the choice of these senser, describes their recent evolution and on-going development, and summarizes their use in demonstrations of autonomous UGV navigation over the past five years. This work has been the first to show that stereo vision can be practical for autonomous UGV navigation, and is now the first to show a real-time terrain classification system with very. low computing requirements., |
