Vision-based global localization based on a hybrid map representation

Ju Hong Park, Soohwan Kim, Nakju Lett Doh, Sung Kee Park

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

    5 Citations (Scopus)

    Abstract

    In this paper we propose a novel vision-based global localization method based on a hybrid map representation. We employ PCA-SIFT features as visual landmarks and represent the environment with a hybrid map which consists of a global topological map and local metric maps. To localize where a mobile robot is placed, we extract visual features from the currently captured view and match them to the feature database previously constructed according to the hybrid map representation. After filtering noise, we estimate the robot's pose with the qualified matching features by the RANSAC approach. We implemented the proposed method in a real mobile robot and tested in both a home-like room and an office-like corridor. The experimental results show that our vision-based global localization system is acceptable in terms of processing time and accuracy.

    Original languageEnglish
    Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
    Pages1104-1108
    Number of pages5
    DOIs
    Publication statusPublished - 2008
    Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
    Duration: 2008 Oct 142008 Oct 17

    Publication series

    Name2008 International Conference on Control, Automation and Systems, ICCAS 2008

    Other

    Other2008 International Conference on Control, Automation and Systems, ICCAS 2008
    Country/TerritoryKorea, Republic of
    CitySeoul
    Period08/10/1408/10/17

    Keywords

    • Global localization
    • Hybrid map representation
    • Mobile robot
    • PCA-SIFT

    ASJC Scopus subject areas

    • Control and Systems Engineering

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