A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.