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Safe, robust operation of an autonomous vehicle in cross-country environments relies on sensing of the surroundings. Thanks to the reduced cost of vision hardware, and increasing computational power, computer vision has become an attractive alternative for this task. This paper concentrates on the use of stereo vision for navigation in cross-country environments. For visual navigation the Scale Invariant Feature Transform, SIFT, is used to locate interest points that are matched between successive stereo image pairs. In this way the ego-motion of a autonomous platform may be estimated by least squares estimation of the interest points in current and previous frame. The paper investigate the situation where GPS become unreliable due to occlusion from for example trees. In this case, however, SIFT based navigation has the advantage that it is possible to locate sufficient interest points close to the robot platform for robust estimation of its ego-motion. In contrast GPS may provide very stable navigation in an open cross-country environment where the interest points from the visual based navigation are sparse and located far from the robot and hence gives a very uncertain position estimate. As a result the paper demonstrates that a combination of the two methods is a way forward for development of robust navigation of robots in a cross country environment.

INVESTIGATING THE POTENTIAL COMBINATION OF GPS AND SCALE INVARIANT VISUAL LANDMARKS FOR ROBUST OUTDOOR CROSS-COUNTRY NAVIGATION