A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers
This work addresses localization challenges for planetary rovers in low-featured environments, but it is incremental as it compares existing filter types without introducing a new method.
This paper tackled the problem of improving wheel-inertial odometry for planetary rovers on rough terrain by comparing adaptive and robust Kalman filters, finding that variational filters achieved better accuracy and maintained localization over longer distances with reduced drift.
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.