Abstract
This paper deals with the Simultaneous Localization and Mapping (SLAM) problem using Extended Kalman Filter. We analyze the convergence of the error covariance matrix of SLAM problems via EKF for the two cases. One is a stationary robot case and the other is a moving robot case. In simulation results evaluate the correctness of derived theorems for the convergence properties of the error covariance matrices. In experimental results, robot's state and environment information can be precisely estimated. Finally, the effectiveness of EKF-SLAM was shown.
