B. de Boer
- Tackling the premature convergence problem in Monte-Carlo localization
- Robotics and Autonomous Systems
- Volume | Issue number
- 57 | 11
- Pages (from-to)
- Number of pages
- Document type
- Faculty of Humanities (FGw)
- Amsterdam Center for Language and Communication (ACLC)
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.
- go to publisher's site
If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library, or send a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.