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Carnegie Mellon Robotics Institute

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Abstract

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.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

L. Matthies, Alonzo Kelly, T. Litwin, and G. Tharp, "Obstacle detection for unmanned ground vehicles: a progress report," Proceedings of IEEE Intelligent Vehicles `95 Conference, September, 1995, pp. 66 - 71.

BibTeX Reference

@inproceedings{Kelly_1995_3612,
   author = "L. Matthies and Alonzo Kelly and T. Litwin and G. Tharp",
   title = "Obstacle detection for unmanned ground vehicles: a progress report",
   booktitle = "Proceedings of IEEE Intelligent Vehicles `95 Conference",
   pages = "66 - 71",
   month = "September",
   year = "1995",
}

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