This paper presents an alternative solution to simultaneous localization and mapping (SLAM) problem by applying a fuzzy Kalman filter using a pseudolinear measurement model of nonholonomic mobile robots. Takagi-Sugeno fuzzy model based on an observation for a nonlinear system is adopted to represent the process and measurement models of the vehicle-landmark system. The complete system of the vehiclelandmark model is decomposed into several linear models. Using the Kalman filter theory, each local model is filtered to find the local estimates. The linear combination of these local estimates gives the global estimate for the complete system. The simulation results shows that the new approach performs better, though nonlinearity is directly involved in the Kalman filter equations, compared to the conventional approach.