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

Journal of Beijing Institute of Technology ›› 2021, Vol. 30 ›› Issue (zk) : 23 -34.

PDF (1655KB)
Journal of Beijing Institute of Technology ›› 2021, Vol. 30 ›› Issue (zk) : 23 -34. DOI: 10.15918/j.jbit1004-0579.20094

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

Author information +
History +
PDF (1655KB)

Abstract

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.

Keywords

grey wolf optimizer / simultaneous localization and mapping (SLAM) / Rao-Blackwellized particle filter (RBPF)

Cite this article

Download citation ▾
null. Improved Grid-Based Rao-Blackwellized Particle Filter SLAM Based on Grey Wolf Optimizer. Journal of Beijing Institute of Technology, 2021, 30(zk): 23-34 DOI:10.15918/j.jbit1004-0579.20094

登录浏览全文

4963

注册一个新账户 忘记密码

References

AI Summary AI Mindmap
PDF (1655KB)

749

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/