This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is coming from the original model has always been invaluable in which high accuracy is expected. Thus

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... o accomplish the above highlighted point, pseudolinear model based Kalman filter (PLKF) state estimator is introduced. A less error prone vehicle process model is proposed to improve the accuracy and the faster convergence of state estimation. Evolution of vehicle motion is modeled using vehicle frame translation derived from successive dead reckoned poses as a control input. A measurement model with two sensor frames is proposed to improve the data association. The PLKF-based SLAM algorithm is simulated using Matlab for vehicle-landmarks system and results show that the proposed approach performs much accurately compared to the well known extended Kalman filter (EKF).