A Comparison of Robust Kalman Filters for Improving Wheel-Inertial Odometry in Planetary Rovers
West Virginia University（西弗吉尼亚大学）；The Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
