Effects of iteration in kalman filters family for improvement of estimation accuracy in simultaneous localization and mapping

In this paper we investigate the role of iteration in kalman filters family for improvement of the estimation accuracy of states in Simultaneous Localization and Mapping (SLAM). The linearized error propagation existing in kalman filters family can result in large errors and inconsistency in the SLAM problem. One approach to alleviate this situation is the use of iteration in Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will describe that the iterated versions of kalman filters can increase the estimation accuracy and robustness of these filters against linear error propagation. Simulation results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear model in EKFSLAM and SPKFSLAM algorithms.