A NOVEL MAP BUILDING METHOD AND EKF BASED LOCALIZATION FOR MOBILE ROBOT

Simultaneous Localization and Mapping (SLAM) algorithms with autonomous robots have received considerable attention in recent years. In general, those algorithms use odometry information and measurements from exteroceptive sensors of robots. The accuracy of the map building and the robot localization algorithms directly affect the overall success of the system. This paper proposed a novel method in map building and robot localization. Unlike traditional approach in building maps, which uses the recursive algorithm and is of low efficiency and occupies much memory, a gradient based algorithm is proposed which has the advantages of concise form, high efficiency and accuracy. Several experiments are taken to demonstrate the comparisons between the ordinary method and the novel one. In localization, Kalman Filter (KF) can evaluate the state of the system from the dynamic circumstances, but it must obey the rule that the noise is of Gaussian distribution, so the modified form (EKF) is adopted on many occasions. Compared to other algorithms such as Markov localization, Mont Carlo localization, EKF is more accurate. By taking the experiments, the applicability of the algorithm is verified. All these techniques have been implemented on mobile robot Pioneer P3-AT equipped with a 2D laser range scanner SICK LMS 200.