Effective application of Monte Carlo localization for service robot

Guanghui Cen, Hideichi Nakamoto, Nobuto Matsuhira, Ichiro Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007
Pages1914-1919
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
Externally publishedYes
Event2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007 - San Diego, CA, United States
Duration: 2007 Oct 292007 Nov 2

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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