Abstract
Robust navigation for mobile robots over long distances requires an accurate method for tracking the robot position in the environment. Techniques for position estimation by determining the camera ego-motion from monocular or stereo sequences have been previously described. However, long-distance navigation requires a very high level of robustness and a very low rate of error growth. In this paper, we describe a methodology for long-distance rover navigation that meets these goals using robust estimation. We show that a system based on only camera ego-motion estimates will accumulate errors with super-linear growth in the distance travelled, owing to increasing orientation errors. When an absolute orientation sensor is incorporated, the error growth can be reduced to a linear function of the distance travelled. We tested these techniques using both extensive simulation and hundreds of real rover images and achieved a low, linear rate of error growth.
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