(Universidad Nacional del Centro de la Provincia de Buenos Aires, 2012) Pérez Paina, Gonzalo; Paz, Claudio; Baudino, Martín; Delfino, Ariel; Destefanis, Eduardo
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
