Modified Particle Filter Algorithm for Mobile Robot Simultaneous Localization and Mapping

摘要

The ability to simultaneous localization and mapping (SLAM) is a predetermination of autonomous mobile robots.Rao-Blackwellised panicle filtering (RBPF) SLAM is a linear time algorithm proportional to number of landmarks,and this algorithm has obvious computational superiority for dense map or large-scale SLAM.To mobile robot SLAM,an improved Rao-Blackwellised particle filter (IRBPF) algorithm is proposed,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,to avoid the depletion problem and decide which weight of the particles are reserved,regularized particle filtering (RPF) are used in IRBPF to re-sample in continuous approximate distributions,so a consistency RBPF SLAM are obtained.Thirdly,a robust motion model and an observation model with only ranging sensor and odometer are constructed.Experimental results show that IRBPF-SLAM performs well on both weight variance and the number of effective samples.More over,the estimation accuracy of path and map is improved to some extent,and the simulation results also indicate that the methods are valid.