Efficient Grid-Based Rao-Blackwellized Particle Filter SLAM with Interparticle Map Sharing

Hyunggi Jo, Hae Min Cho, Sungjin Jo, Euntai Kim

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.

Original languageEnglish
Pages (from-to)714-724
Number of pages11
JournalIEEE/ASME Transactions on Mechatronics
Volume23
Issue number2
DOIs
Publication statusPublished - 2018 Apr

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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