Square-Root Cubature Kalman Filter and Its Application to SLAM of an Mobile Robot

Abstract

For simultaneous localization and mapping (SLAM) of robots, a new solution is proposed, named square-root cubature Kalman filter based SLAM algorithm (SCKF-SLAM). The main contribution of the proposed algorithm is that the SLAM posterior probability density is calculated by using the square root cubature Kalman filter in order to reduce linearization error and improve SLAM accuracy. Instead of covariance matrixes, square-root factors are used in the proposed SLAM algorithm to avoid the time-consuming Cholesky decompositions and improve the calculation efficiency. In experiments, the proposed algorithm is compared with extended Kalman filter SLAM (EKF-SLAM) and unscented Kalman filter SLAM (UKF-SLAM). The results show that compared with EKF-SLAM, precision of SCKF-SLAM is doubled, and compared with UKF-SLAM, SCKF-SLAM saves a quarter of computation resources.