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Jun. 2021
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Xiaolin Dai, Xuhong Sun, Jiacheng He, Anxu Li, Dawei Gong. Improved Grid-Based Rao-Blackwellized Particle Filter SLAM Based on Grey Wolf Optimizer[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2021, 30(zk): 23-34. doi: 10.15918/j.jbit1004-0579.20094
Citation: Xiaolin Dai, Xuhong Sun, Jiacheng He, Anxu Li, Dawei Gong. Improved Grid-Based Rao-Blackwellized Particle Filter SLAM Based on Grey Wolf Optimizer[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2021, 30(zk): 23-34.doi:10.15918/j.jbit1004-0579.20094
shu

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

doi:10.15918/j.jbit1004-0579.20094

  • University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:Sichuan Science and Technology Project (2020YFG0345,2020YFQ0023)
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  • Corresponding author:professor, Ph.D. E-mail:pzhzhx@126.com
  • Received Date:2020-06-16
  • Publish Date:2021-06-30
    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.
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