Genetic Bayesian ARAM for simultaneous localization and hybrid map building

Wei Hong Chin, Chu Kiong Loo, Naoyuki Kubota, Yuichiro Toda

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

Abstract

This paper presents a new framework for mobile robot to perform localization and build topological-metric hybrid map simultaneously. The proposed framework termed as Genetic Bayesian ARAM consists of two main components: 1) Steady state genetic algorithm (SSGA) for self-localization and occupancy grid map building and 2) Bayesian Adaptive Resonance Associative Memory (ARAM) for topological map building. The proposed method is validated using a mobile robot. Result show that Genetic Bayesian ARAM capable of generate hybrid map online and perform localization simultaneously.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages275-279
Number of pages5
ISBN (Electronic)9781479975600
DOIs
Publication statusPublished - Jan 1 2015
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence, SSCI 2015 - Cape Town, South Africa
Duration: Dec 8 2015Dec 10 2015

Other

OtherIEEE Symposium Series on Computational Intelligence, SSCI 2015
CountrySouth Africa
CityCape Town
Period12/8/1512/10/15

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Chin, W. H., Loo, C. K., Kubota, N., & Toda, Y. (2015). Genetic Bayesian ARAM for simultaneous localization and hybrid map building. In Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 (pp. 275-279). [7376621] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2015.48