Efficient and reliable Monte Carlo localization with thinning edges

Tae Bum Kwon; Ju Ho Yang; Jae Bok Song

doi:10.1007/s12555-010-0219-3

Efficient and reliable Monte Carlo localization with thinning edges

Tae Bum Kwon, Ju Ho Yang, Jae Bok Song

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The global convergence of MCL is time-consuming because of a large number of random samples. Moreover, its success is not guaranteed at all times. This paper presents a novel approach to reduce the number of samples of MCL and one heuristic approach to detect localization failure using thinning edges extracted in real time. Random samples are drawn only around the neighborhood of the thinning edges rather than over the entire free space and localization quality is estimated through the probabilistic analysis of samples added around the thinning edges. A series of experiments verified the performance of the proposed scheme.

Original language	English
Pages (from-to)	328-338
Number of pages	11
Journal	International Journal of Control, Automation and Systems
Volume	8
Issue number	2
DOIs	https://doi.org/10.1007/s12555-010-0219-3
Publication status	Published - 2010 Apr

Keywords

Kidnapped robot problems
Monte carlo localization
Particle filters
Thinning edges

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications

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10.1007/s12555-010-0219-3

Cite this

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title = "Efficient and reliable Monte Carlo localization with thinning edges",

abstract = "The global convergence of MCL is time-consuming because of a large number of random samples. Moreover, its success is not guaranteed at all times. This paper presents a novel approach to reduce the number of samples of MCL and one heuristic approach to detect localization failure using thinning edges extracted in real time. Random samples are drawn only around the neighborhood of the thinning edges rather than over the entire free space and localization quality is estimated through the probabilistic analysis of samples added around the thinning edges. A series of experiments verified the performance of the proposed scheme.",

keywords = "Kidnapped robot problems, Monte carlo localization, Particle filters, Thinning edges",

author = "Kwon, {Tae Bum} and Yang, {Ju Ho} and Song, {Jae Bok}",

note = "Funding Information: __________ Manuscript received November 4, 2008; revised July 17, 2009; accepted September 1, 2009. Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Jae Weon Choi. This research was supported in part by the Intelligent Robotics Development Program (21st Century Frontier R&D Program) and in part by the Agency for Defense Development and Unmanned Technology Research Center at KAIST.",

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doi = "10.1007/s12555-010-0219-3",

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N2 - The global convergence of MCL is time-consuming because of a large number of random samples. Moreover, its success is not guaranteed at all times. This paper presents a novel approach to reduce the number of samples of MCL and one heuristic approach to detect localization failure using thinning edges extracted in real time. Random samples are drawn only around the neighborhood of the thinning edges rather than over the entire free space and localization quality is estimated through the probabilistic analysis of samples added around the thinning edges. A series of experiments verified the performance of the proposed scheme.

AB - The global convergence of MCL is time-consuming because of a large number of random samples. Moreover, its success is not guaranteed at all times. This paper presents a novel approach to reduce the number of samples of MCL and one heuristic approach to detect localization failure using thinning edges extracted in real time. Random samples are drawn only around the neighborhood of the thinning edges rather than over the entire free space and localization quality is estimated through the probabilistic analysis of samples added around the thinning edges. A series of experiments verified the performance of the proposed scheme.

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KW - Particle filters

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Efficient and reliable Monte Carlo localization with thinning edges

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Keywords

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