Simultaneous localization and map building based on improved particle filter in grid map

The ability to simultaneous localization and precise mapping is a predetermination of autonomous robots. But because of the unknown location, the unpredictable environment information, the method of simultaneous localization and map building based on improved Particle Filter in grid map is presented to solve these uncertain problems. The distribution over robot poses and map information is estimated with Bayes´ rules and the improved Particle Filter respectively. The simulation result shows the method reduces the complexity of the data and enhances the real-time performance of the improved algorithm. With the method, the robot localizes itself accurately as well as builds grid map with higher accuracy. The proposed algorithm is effective and reliable to realize simultaneous localization and mapping.