Implementation and performance evaluation of UKF for Simultaneous Localization and Mapping

Abstract

The implementation of the Unscented Kalman Filter (UKF) for SLAM estimation is described. The UKF presents comparatively lower linearization error with respect to the typically used Extended Kalman Filter (EKF). The algorithm described in detail implements an UKFSLAM to build a feature-based map representation, for a mobile robot using a laser rangefinder. An evaluation comparing the UKF/EKF-SLAM estimations is shown. Results demonstrate a better performance of the UKF in terms of both robot pose and map feature positions estimation, besides the fact that UKF is easier to implement than EKF