Accurate localization with COAG features and self-adaptive energy region

Dong Il Kim, Jae-Bok Song

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

Abstract

Localization is very important for autonomous navigation of a mobile robot. For outdoor localization, Monte Carlo Localization (MCL) is used with the digital surface model. In order to develop an improved localization technique, in this study, commonly observed from air and ground (COAG) features are incorporated into an MCL localization system by means of an energy function to determine candidates. Experiments in real environments show improved localization accuracy over methods using MCL with COAG features.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages576-583
Number of pages8
Volume8102 LNAI
EditionPART 1
DOIs
Publication statusPublished - 2013 Oct 7
Event6th International Conference on Intelligent Robotics and Applications, ICIRA 2013 - Busan, Korea, Republic of
Duration: 2013 Sep 252013 Sep 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8102 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Conference on Intelligent Robotics and Applications, ICIRA 2013
CountryKorea, Republic of
CityBusan
Period13/9/2513/9/28

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Keywords

  • MCL
  • Monte Carlo Localization

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kim, D. I., & Song, J-B. (2013). Accurate localization with COAG features and self-adaptive energy region. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 8102 LNAI, pp. 576-583). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8102 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-40852-6-58