Simultaneous localization and mapping for mobile robot based on an improved particle filter algorithm

Abstract

Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. An improved Rao-Blackwellised particle filter (IRBPF) algorithm is proposed for the mobile robot to SLAM, which can simultaneously localize the robot and build up the map in the structured indoor environment. Firstly, IRBPF respectively uses particle filters (PF) to estimate the posterior probability distributions of robot postures and landmarks in the environment map. Secondly, an adaptive re-sampling technique is used to reduce the times of re-sampling so as to maintain a reasonable speed of samples, thus it reduce the risk of sample depletion. Finally, a robust motion model and an observation model with only ranging sensor and odometer are constructed. Experiment results indicate that the IRBPF algorithm builds the consistent map and modified the precision and real-time performance of localization and mapping, the SLAM results show the efficiency of this IRBPF algorithm.