Mobile robot global localization using particle filters

Guanghui Cen, Nobuto Matsuhira, Junko Hirokawa, Hideki Ogawa, Ichiro Hagiwara

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

13 Citations (Scopus)

Abstract

Mobile robot global localization is the problem of determining a robot's pose in an environment by using sensor data, when the initial position is unknown. Particle filter based Probabilistic algorithm called Monte Carlo Localization is the current popular approach to solve the robot localization problem. 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. We also proposed likelihood based particle filter to solve the kidnapped problem. The experiment results illustrate the efficiency and robustness of particle filter approach for our mobile robot.

Original languageEnglish
Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
Pages710-713
Number of pages4
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
Duration: 2008 Oct 142008 Oct 17

Publication series

Name2008 International Conference on Control, Automation and Systems, ICCAS 2008

Conference

Conference2008 International Conference on Control, Automation and Systems, ICCAS 2008
CountryKorea, Republic of
CitySeoul
Period08/10/1408/10/17

Keywords

  • Global localization
  • Likelihood
  • Mobile robot
  • Particle filter

ASJC Scopus subject areas

  • Control and Systems Engineering

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  • Cite this

    Cen, G., Matsuhira, N., Hirokawa, J., Ogawa, H., & Hagiwara, I. (2008). Mobile robot global localization using particle filters. In 2008 International Conference on Control, Automation and Systems, ICCAS 2008 (pp. 710-713). [4694593] (2008 International Conference on Control, Automation and Systems, ICCAS 2008). https://doi.org/10.1109/ICCAS.2008.4694593