This paper proposed an approach of Fuzzy-Extended Kalman Filter (FEKF) for mobile robot localization and mapping considering unknown noise characteristics. The techniques apply the information extracted from EKF measurement innovation to derive the best output for mobile robot estimation during its observations. This information is then fuzzified using Fuzzy Logic technique, designed with very few design rules to control the information. The method can further reduced measurement error and as a result provides better localization and mapping. Simulation results are also presented to describe the efficiency of the proposed method in comparison with the normal EKF estimation. Preliminary results emphasize that FEKF has exceeds the estimation results performance of normal EKF in non-Gaussian noise environment.