The Study of Improving Kalman Filters Family for Nonlinear SLAM

When Extended Kalman Filter is used to solve the SLAM problem of a nonlinear system, the linearization error will lead to severe estimation error or even make the method to be divergent. After analyzing the linearization principle of Kalman filters family, two improved methods are suggested to decrease the linearization error. These two methods improve posterior estimation accuracy by revising the observation-update step. Simulation results indicate that the two methods are feasible. The method named 'Mean Extended Kalman Filter' performs much better than EKF and UKF for nonlinear SLAM. And the iterated version of EKF and UKF even falls behind MEKF in estimation accuracy. In addition, MEKF is computationally efficient. With a view to both estimation accuracy and computational complexity, MEKF seems to be the best filter of the Kalman filters family for nonlinear SLAM. Experiments are carried out with 'Car Park Dataset' and 'Victoria Park Dataset' to evaluate the performance of MEKF based SLAM solutions. And the experimental results validate the effectiveness of MEKF in real SLAM applications.