Curb detection and accessibility evaluation from low-density mobile mapping point cloud data

Kiichiro Ishikawa, Daisuke Kubo, Yoshiharu Amano

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    Our goal is to automatically classify objects from Mobile Mapping System data to enable the automatic construction of dynamic maps. We aimed at the extraction of curbstones and classification of curb types. Although there is much research about curbstones being recognized from laser-scanned point clouds, there are few methods to classify curb types. In this paper, we propose a method to extract curbstones from low-density-type laser scan data. We also propose a method to distinguish whether curbstones allow access to off-road facilities. Evaluation tests give an F-measure of ≥94.4% and an accessibility classification accuracy of ≥99.6%. Moreover, the results of applying multiple filters to noise removal are compared.

    Original languageEnglish
    Pages (from-to)376-385
    Number of pages10
    JournalInternational Journal of Automation Technology
    Volume12
    Issue number3
    DOIs
    Publication statusPublished - 2018 May 1

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    Curbs
    Lasers

    Keywords

    • Autonomous drive
    • Curb accessibility
    • Dynamic map
    • Mobile mapping system

    ASJC Scopus subject areas

    • Mechanical Engineering
    • Industrial and Manufacturing Engineering

    Cite this

    Curb detection and accessibility evaluation from low-density mobile mapping point cloud data. / Ishikawa, Kiichiro; Kubo, Daisuke; Amano, Yoshiharu.

    In: International Journal of Automation Technology, Vol. 12, No. 3, 01.05.2018, p. 376-385.

    Research output: Contribution to journalArticle

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