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The self-localization of mobile robots in unknown environments is one of the most fundamental problems in robotics navigation. This is a complex problem because of the strict requirements on mobile robots, especially regarding to algorithm accuracy, robustness and computational efficiency. The purpose of this paper is to demonstrate a real time 2D mapping and localization approach for robotic application based on Extended Kalman Filter (EKF), Particle Filter and Iterative Closest Point (ICP). The loop closure method is added and shows satisfactory 2D mapping and localization results. We test our approach on large building simulations with large odometry positioning errors. A two-wheel differential drive mobile robot equipped with a 0.02 m resolution Laser Range Finder is used for testing. Simulation results show that accurate maps of large, cyclic environments can be generated even if there are no odometric data. Real time mapping can reach a resolution of 5 cm. This work can help in developing real-time mapping for robotic applications that process 2D cloud points in real time.