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

Masashi Yokozuka*, Osamu Matsumoto

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1649-1675
ページ数27
ジャーナルAdvanced Robotics
26
14
DOI
出版ステータスPublished - 2012 9 1
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用

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