Robust navigation for mobile robots over long distances requires an accurate method for tracking the robot position in the environment. Promising techniques for position estimation by determining the camera ego-motion from monocular or stereo sequences have been previously described. However, long-distance navigation requires both a high level of robustness and a low rate of error growth. In this paper, we describe a methodology for long-distance rover navigation that meets these goals using robust estimation of ego-motion. The basic method is a maximum-likelihood ego-motion algorithm that models the error in stereo matching as a normal distribution elongated along the (parallel) camera viewing axes. Several mechanisms are described for improving navigation robustness in the context of this methodology. In addition, we show that a system based on only camera ego-motion estimates will accumulate errors with super-linear growth in the distance traveled, 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 traveled. We have tested these techniques using both extensive simulation and hundreds of real rover images and have achieved a low, linear rate of error growth. This method has been implemented to run on-board a prototype Mars rover.


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    Title :

    Rover navigation using stereo ego-motion


    Contributors:

    Published in:

    Publication date :

    2003


    Size :

    15 Seiten, 27 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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