Simultaneous localization and mapping based on multilevel-EKF

Simultaneous localization and mapping (SLAM) problem is an attractive topic in the mobile vehicle research. It is a navigation algorithm essentially. Extended Kalman Filter (EKF) is the most popular implementation to solve the SLAM problem for its simpleness and effectiveness. But the linearization errors of EKF are inevitable because of many factors like inaccuracy of the system model. Moreover, the estimated precision will be depressed because a moving vehicle will inherently accumulate errors in its position estimate as a result of the noise introduced in the dead-reckoning. In order to reduce the linearization errors and improve the estimated precision, a new SLAM algorithm based on multilevel-EKF is developed. Simulation demonstrated that the accuracy of multilevel-EKF-SLAM is superior to the standard EKF-SLAM. It not only weakened the influence of linearization greatly but also improved the estimated accuracy.