Improved Grid-Based Rao-Blackwellized Particle Filter SLAM Based on Grey Wolf Optimizer

In this work, a Rao-Blackwellized particle filter simultaneous localization and mapping based on grey wolf optimizer (called GWO-RBPF) is proposed. The proposed method aims to improve the accuracy of the mapping while maintaining the number of particles. GWO-RBPF utilizes the local exploration and global development ability of the grey wolf optimizer to improve the estimation performance of the Rao-Blackwellized particle filter, so that the low-weight particles can approach high-weight particles. Meanwhile, the pose information of the particles is optimized by the grey wolf optimizer. The proposed method is applied to the benchmark datasets and real-world datasets. The experimental results show that our method outperforms conventional method in terms of map accuracy versus the number of particles.