In this paper, by introducing development of two approaches based on the relative map filter (RMF), attempts have been made to improve simultaneous localization and mapping (SLAM). The implementation of Extended Kalman Filter SLAM (EKF-SLAM) in large environments is not practical due to the large volume of calculations. On the other hand, the observation and motion models of many robots are nonlinear and these cause the divergence of EKF-SLAM. The basis of RMF is relative distances between landmarks; therefore its equations are independent from the robot motion model.
Also, the robot observation model can be linearly defined and its convergence is guaranteed. Despite these features, the relative filter proposed methods are faced with the problem of ambiguity in absolute positioning of robot and landmarks. In this article, ILPE (Improved Lowest Position Estimation) and IMVPE (Improved Minimum Variance Position Estimation) methods are introduced. In these methods, the ambiguity problem in localization and mapping of robot and landmarks are solved by sequential switching between absolute and relative spaces. The calculation volume of these methods does not depend on the number of landmarks but is contingent on the average number of landmarks observed in each scan of the robot. In this paper, the equations and the required algorithm to find the position of landmarks and robot are presented. Moreover, by simulation, the performance and efficiency of the proposed methods are discussed in comparison with the previous methods including EKF-SLAM.