Monte Carlo Localization Incorporating an Error Correction Vector for Mobile Robots

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

Abstract

Nowadays, Monte Carlo Localization (MCL) algorithm is the most commonly used algorithm for a mobile robot to localize itself automatically, and has shown good performances because of its ability to model arbitrary distributions and robustness towards noisy input data. This paper presents an improved Monte Carlo Localization algorithm incorporating an error correction vector, which is calculated from the sensor's error information, and therefore, can achieve better performances in both accuracy and efficiency. Through practical application of the method which incorporates the error correction vector when conducting the program's prediction stage, the particles move closer to the real robot's position. Experimental results show that the proposed algorithm can increase the probability for particles to better estimate the robot's position.

Original languageEnglish
Title of host publicationNew Trends on System Sciences and Engineering - Proceedings of ICSSE 2015
EditorsHamido Fujita, Shun-Feng Su
PublisherIOS Press
Pages306-318
Number of pages13
ISBN (Electronic)9781614995210
DOIs
Publication statusPublished - 2015 Jan 1
EventInternational Conference on System Science and Engineering, ICSSE 2015 - Morioka, Japan
Duration: 2015 Jul 62015 Jul 8

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume276
ISSN (Print)0922-6389

Other

OtherInternational Conference on System Science and Engineering, ICSSE 2015
CountryJapan
CityMorioka
Period15/7/615/7/8

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Keywords

  • Monte Carlo localization
  • error correction vector
  • particle filter

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Kung, Y. C., Hsu, C-C. J., & Wang, W-Y. (2015). Monte Carlo Localization Incorporating an Error Correction Vector for Mobile Robots. In H. Fujita, & S-F. Su (Eds.), New Trends on System Sciences and Engineering - Proceedings of ICSSE 2015 (pp. 306-318). (Frontiers in Artificial Intelligence and Applications; Vol. 276). IOS Press. https://doi.org/10.3233/978-1-61499-522-7-306