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

Shounak Das, Cagri Kilic, Ryan Watson, and Jason Gross

Abstract: 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.
Published in: Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021)
September 20 - 24, 2021
Union Station Hotel
St. Louis, Missouri
Pages: 2621 - 2632
Cite this article: Das, Shounak, Kilic, Cagri, Watson, Ryan, Gross, Jason, "A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers," Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 2621-2632.
https://doi.org/10.33012/2021.17938
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