Research on Rao-Blackwellized Particle Filter SLAM Based On Grey Wolf Optimizer

In order to improve the accuracy of localization and mapping, this paper proposes a Rao-Blackwellized particle filter SLAM based on grey wolf optimizer. The estimation performance of the Rao-Blackwellized particle filter is improved by the local exploration and global exploitation capabilities of the grey wolf optimizer. Low-weight particles move towards the “prey” and the estimates of the pose is further optimized in the process. The algorithm overcomes the problem of particle degradation to a certain extent and reduces the number of particles required for precise localization and mapping. The improved algorithm is compared with the gmapping on different data sets and the results prove that the proposed algorithm is effective in different environments.