Sub-map dividing and realignment FastSlam by blocking Gibbs Mcem for large-scale 3-d grid mapping

Masashi Yokozuka, Osamu Matsumoto

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In this article, we propose a novel Simultaneous Localization and Mapping (SLAM) method by using a sampling-based approach. FastSLAM is well-known approach as a sampling-based SLAM method. FastSLAM utilizes a theorem that map errors are decidable under a sample of trajectories. From this theorem, FastSLAM samples many trajectories and maps to find a minimum error map. However, in case of constructing a large-scale grid map, FastSLAM becomes unuseful since the method requires huge memory to generate many grid maps. Our proposed method requires only one map that is deformable corresponding to a trajectory. In order to find a minimum error map, our method only generates trajectories. Our method enables construction of a minimum error map by little memory in comparison with original FastSLAM. Experimental results demonstrate our method is able to construct a large-scale 3-D grid map by low memory usage in comparison with original FastSLAM.

Original languageEnglish
Pages (from-to)1649-1675
Number of pages27
JournalAdvanced Robotics
Volume26
Issue number14
DOIs
Publication statusPublished - 2012 Sep 1
Externally publishedYes

Keywords

  • 3-D grid map
  • FastSLAM
  • mobile robot
  • SL

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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