Journal of Intelligent and Robotic Systems

, Volume 47, Issue 2, pp 155–174 | Cite as

Monte Carlo Filter in Mobile Robotics Localization: A Clustered Evolutionary Point of View

  • Andrea Gasparri
  • Stefano Panzieri
  • Federica Pascucci
  • Giovanni Ulivi


Localization, i.e., estimating a robot pose relative to a map of an environment, is one of the most relevant problems in mobile robotics. The research community has devoted a big effort to provide solutions for the localization problem. Several methodologies have been proposed, among them the Kalman filter and Monte Carlo Localization filters. In this paper, the Clustered Evolutionary Monte Carlo filter (CE-MCL) is presented. This algorithm, taking advantage of an evolutionary approach along with a clusterization method, is able to overcome classical MCL filter drawbacks. Exhaustive experiments, carried on the robot ATRV-Jr manufactured by iRobot, are shown to prove the effectiveness of the proposed CE-MCL filter.

Key words

clustering genetic algorithms Monte Carlo integration methods  robot localization 


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Andrea Gasparri
    • 1
  • Stefano Panzieri
    • 1
  • Federica Pascucci
    • 1
  • Giovanni Ulivi
    • 1
  1. 1.Dipartimento di Informatica e AutomazioneUniversitá “Roma Tre”RomaItaly

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