Range scan matching and Particle Filter based mobile robot SLAM

This paper presents an effective Simultaneous Localization and Map-Building (SLAM) technique for indoor mobile robot navigation based on laser scan-matching and Rao-Blackwellized Particle Filter (RBPF). Although the Extended Kalman Filter (EKF) solution exhibits some desirable properties, the associated geometric feature map itself fails to cope with senor noise mingled in the incoming laser reading and unable to serve in the environment absent of such features as straight lines and corners. Compared with FastSLAM, main advantage of our work is the smart extension that is made to deal with sensor uncertainty by using recursive Bayesian updating based occupancy grid map management. Furthermore, to improve the environment compatibility, we presented a dense laser scan matching approach which allows handling various type of environment. Advantages of our proposal are validated by real experimental results carried on Pioneer robot.