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

TL;DR

This paper evaluates various Kalman filter algorithms, including variational methods, for enhancing wheel-inertial odometry in planetary rovers, demonstrating improved accuracy and robustness on rough terrain.

Contribution

It introduces and experimentally compares variational Kalman filters with classical methods for robust localization in planetary rover navigation.

Findings

Variational filters outperform classical adaptive filters in accuracy.

Variational filters effectively handle erroneous measurements and reduce drift.

Parameter tuning significantly impacts localization performance.

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.