Robots generally divided into two categories, based mobile and fixed base. Movable base or mobile robots cover a wide range of human needs. Mobile robots are used for planetary exploration, exploring areas that are harmful to humans and some other applications.One of the main goals in the field of autonomous mobile robots is proposed; in order to explore an unknown environment, robot-generated map of the environment, detect the presence of specific symptoms in environment (vital life signs, warning signs and risk factors, environmental pollution and etc.) and accurate map of their location in the environment.To achieve this goal the fundamental issues are: determine unexplored areas of the environment, routing to guide the robot (to identify passable routes for delivering the robot to unexplored areas and avoid of obstacles) and robot control. Scientific issues related to mobile robotics includes subjects such as, their understanding of the environment and how show it, Traveling and exploring the unknown environment and changing environment with artificial agents which are controlled by the computers. There are uncertainties in solving problems in mobile robotics. Generally, this phenomenon is permanently present in many robotic problems, in some cases can be irrespective and in the other issues to deal with them with the appropriate tools. A branch of the robotics which has been considered in the recent decades is a probabilistic robotic. This field of robotic explores issues which involved with such uncertainties. Simultaneously localization and mapping raised this question that is it possible for an autonomous vehicle starting to move in an unknown location in an unknown environment well, and then gradually begin to build maps of the surrounding environment, while both of these maps are also used to calculate your exact location. The most common approach for solving SLAM is using techniques based on probability theory and Kalman filter. In the past decade, one of the issues about mobile robots that researchers have been interested in, is probabilistic robotic. This knowledge involved in the robotics issues that the uncertainty exists in them. Simultaneously localizationand mapping isone of themost important topicsof probabilistic roboticsand is essential formobilerobots. Inthis thesis,the simultaneously localization and mapping algorithmusingextendedKalmanfilter (EKF-SLAM) isimplemented onarescuemobilerobot. Eliminateconfusionwithinthe datacollection which had been in theaccuracy rangeofthe laser range finder (LRF) sensor of therobots andusing an angular threshold wasimprovedthe lineextraction algorithm of themapping. Also,to improvetheperformance ofiterative closest point (ICP)algorithm, in the associationstep, instead ofthe nearest neighbor search ()method, we used the methodsfor solving theassignment problem. Anotherinnovationofthis thesis is, toimproveestimatedtransformationbyrotaryencoder sensor, beforeits use ininitial prediction step of EKF-SLAM, by ICP algorithm. Thisimprovedprocess iscallediEKF-SLAMalgorithm. . It is possible to show that the two-dimensional concept localization and mapping extend to three dimensions. For this purpose, data extracted from 3D laser scanner must be used. Keywords: Mobile robots, Laser range finder, Localization, Mapping