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Simultaneous Localization and Mapping (SLAM) is one of the most fundamental and challenging problems in mobile robotics. In this paper solving vision based SLAM problem using Kalman filters family have been provided. It is focused on mobile robot equipped with stereo vision sensor which moves in an indoor environment. The mobile robot navigated among the landmarks which were detected by Scale Invariant Feature Transform (SIFT) method. The Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF) approaches have been used to solve this SLAM problem. Then the role of Iteration in these filters to improve estimation state accuracy in SLAM has been investigated. Finally in the experimental results the better state estimation accuracy in iterated EKF and SPKF has been shown.