Effective application of Monte Carlo localization for service robot

Guanghui Cen, Hideichi Nakamoto, Nobuto Matsuhira, Ichiro Hagiwara

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

At indoor environment, a service robot must know where it is at any time. Thus, reliable position estimation is a basic and key problem. Probabilistic robotics techniques have become one of the dominant paradigms for algorithm design in robotics. Recent work on Monte Carlo Localization with particle-based density representation becomes popular. In this paper we introduce the multi-sensor based Monte Carlo Localization (MCL) method which represents a robot's belief by a set of weighted samples and use the Laser Range Finder (LRF) sensor to measurement update. The experiment results illustrate the effectivity and robust of MCL application for our service robot.

本文言語English
ホスト出版物のタイトルProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
ページ1914-1919
ページ数6
DOI
出版ステータスPublished - 2007
外部発表はい
イベント2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
継続期間: 2007 10月 292007 11月 2

出版物シリーズ

名前IEEE International Conference on Intelligent Robots and Systems

Other

Other2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
国/地域United States
CitySan Diego, CA
Period07/10/2907/11/2

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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