SLAM based on Kalman filter for multi-rate fusion of laser and encoder measurements

In this paper, extended Kalman and unscented Kalman filters are developed for multi-rate systems in the context of the SLAM problem. These multi-rate filters have been extensively compared and tested with experimental data taken from a parking lot. Both multi-rate filters have improved the estimation with respect to the conventional single-rate Kalman filter. A performance index is introduced, showing that EKF gives better performance than UKF. In order to complete the SLAM solution, well-known techniques for feature extraction, data association and map building have been also implemented.