Efficiency improvement in Monte Carlo localization through topological information

Tae Bum Kwon, Ju Ho Yang, Jae-Bok Song, Woo Jin Chung

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

25 Citations (Scopus)

Abstract

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Many studies have been conducted to improve performance of MCL. Although MCL is capable of estimating the robot pose when the initial pose of a robot is not given, it takes much time for convergence because a large number of random samples are required, especially for the large-scale environment. For practical implementation of MCL, therefore, it is desirable to reduce the number of samples without affecting the localization performance. This paper presents a novel approach to reduce the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information is extracted in real time through the thinning algorithm from the range data of a laser scanner. The topological map is first created from the given grid map of the environment. The robot scans the local environment and generates a local topological map. The robot then navigates along this local topological edge, which coincides with the global topological map obtained off-line from the given global grid map. By constraining the robot's motion on this local edge, random samples are drawn only around the neighborhood of the topological edge rather than over the entire free space. Hence the sample size required for MCL can be drastically reduced, thereby reducing computational time for the MCL process. A series of experiments based on this proposed MCL/TI show that the number of samples can be reduced considerably, and thus the time required for pose estimation can be substantially decreased.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
Pages424-429
Number of pages6
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing, China
Duration: 2006 Oct 92006 Oct 15

Other

Other2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
CountryChina
CityBeijing
Period06/10/906/10/15

Fingerprint

Robots
Mobile robots
Lasers
Experiments

Keywords

  • Monte Carlo localization
  • Particle filters
  • Topological information

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Kwon, T. B., Yang, J. H., Song, J-B., & Chung, W. J. (2006). Efficiency improvement in Monte Carlo localization through topological information. In IEEE International Conference on Intelligent Robots and Systems (pp. 424-429). [4059089] https://doi.org/10.1109/IROS.2006.281962

Efficiency improvement in Monte Carlo localization through topological information. / Kwon, Tae Bum; Yang, Ju Ho; Song, Jae-Bok; Chung, Woo Jin.

IEEE International Conference on Intelligent Robots and Systems. 2006. p. 424-429 4059089.

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

Kwon, TB, Yang, JH, Song, J-B & Chung, WJ 2006, Efficiency improvement in Monte Carlo localization through topological information. in IEEE International Conference on Intelligent Robots and Systems., 4059089, pp. 424-429, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006, Beijing, China, 06/10/9. https://doi.org/10.1109/IROS.2006.281962
Kwon TB, Yang JH, Song J-B, Chung WJ. Efficiency improvement in Monte Carlo localization through topological information. In IEEE International Conference on Intelligent Robots and Systems. 2006. p. 424-429. 4059089 https://doi.org/10.1109/IROS.2006.281962
Kwon, Tae Bum ; Yang, Ju Ho ; Song, Jae-Bok ; Chung, Woo Jin. / Efficiency improvement in Monte Carlo localization through topological information. IEEE International Conference on Intelligent Robots and Systems. 2006. pp. 424-429
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