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This paper compares the performance of adaptive and robust Kalman filteralgorithms in improving wheel-inertial odometry on low featured rough terrain.Approaches include classical adaptive and robust methods as well as variationalmethods, which are evaluated experimentally on a wheeled rover in terrainsimilar to what would be encountered in planetary exploration. Variationalfilters show improved solution accuracy compared to the classical adaptivefilters and are able to handle erroneous wheel odometry measurements and keepgood localization for longer distances without significant drift. We also showhow varying the parameters affects localization performance.

proceedings of the satellite division's international technical meeting (online)/proceedings of the satellite division's international technical meeting (cd-rom)

A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers