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