Obstacle detection for unmanned ground vehicles: a progress report - Robotics Institute Carnegie Mellon University

Obstacle detection for unmanned ground vehicles: a progress report

L. Matthies, Alonzo Kelly, T. Litwin, and G. Tharp
Conference Paper, Proceedings of IEEE Intelligent Vehicles Symposium (IV '95), pp. 66 - 71, September, 1995

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.

BibTeX

@conference{Matthies-1995-13985,
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 Symposium (IV '95)},
year = {1995},
month = {September},
pages = {66 - 71},
}