New entropy-based adaptive particle filter for mobile robot localization

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Over the last decade, particle filters have been applied with great success to a variety of state estimation problem. The standard particle filter suffers poor efficiency during the estimation process, especially in the global localization and kidnapped problem. In this paper, we proposed a novel information entropy-based adaptive approach to improve the efficiency of particle filters by adapting the number of particles. The information entropy-based adaptive particle filter approaches use the information entropy to present the uncertainty of a mobile robot to the environment. By continuously obtaining the sensor information, the robot gradually reduces the uncertainty to the environment and, therefore, reduces the particle number for the estimation process. We derived the mathematic equation relating the information entropy with particle number. Extensive localization experiments using a mobile robot showed that our approach yielded drastic improvements and efficiency performance over a standard particle filter with fixed particles and over other adaptive approaches.

Original languageEnglish
Pages (from-to)1761-1778
Number of pages18
JournalAdvanced Robotics
Issue number12-13
Publication statusPublished - 2009 Sept 1
Externally publishedYes


  • Adaptive particle filter
  • Global localization
  • Kidnapped problem
  • Mobile robot
  • Mutual information entropy

ASJC Scopus subject areas

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


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