The autonomy is the most crucial criteria in mobile robots. This operation aims to offer the ability of finding the position and build a map of the environment to the robot. Many methods have been proposed to solve this problem. In this study, an implementation of SLAM approach for unknown indoor environment exploring by mobile robot is proposed. In fact, the proposed approach touch on the unknown indoor environments exploring with static obstacles, based on robot mobile abilities (extereoceptive and proprioceptive sensors). In one hand, the measurements given by the proprioceptive sensor (odometry) are used for the auto localization system. In the other hand, the map building based on extereoceptive sensor scanning and robot position. Therefore, the approach maintains two maps: (1) (OM)... map grid describe the occupancy of environment; (2) (TM) map grid memorizes the robot former positions. Furthermore, the use of the proposed maps afford an efficient description and exploitation of the environment resources over time. Finally, the results in simulation and real robots experiments using random exploration (for test), demonstrate the fusibility of the proposed approach.