Tackling the premature convergence problem in Monte-Carlo localization

Gert Kootstra*, Bart de Boer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)


Monte-Carlo localization uses particle filtering to estimate the position of the robot. The method is known to suffer from the loss of potential positions when there is ambiguity present in the environment. Since many indoor environments are highly symmetric, this problem of premature convergence is problematic for indoor robot navigation. It is, however, rarely studied in particle filters. We introduce a number of so-called niching methods used in genetic algorithms, and implement them on a particle filter for Monte-Carlo localization. The experiments show a significant improvement in the diversity maintaining performance of the particle filter.

Original languageEnglish
Pages (from-to)1107-1118
Number of pages12
JournalRobotics and Autonomous Systems
Issue number11
Publication statusPublished - 1 Nov 2009
Externally publishedYes


  • Genetic algorithms
  • Monte-Carlo localization
  • Niching
  • Particle filter
  • Premature convergence

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