Robustifying correspondence based 6D object pose estimation

Antti Hietanen, Jussi Halme, Anders Glent Buch, Jyrki Latokartano, J.-K. Kamarainen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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    We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improves robustness. The second method, region pruning, is more general by making no assumptions about local surface properties. Our region pruning segments a model point cloud into cluster regions and searches good region combinations using a validation set. The robustifying methods are general and can be used with any correspondence based method. For the experiments, we evaluated three correspondence selection methods, Geometric Consistency (GC) [1], Hough Grouping (HG) [2] and Search of Inliers (SI) [3] and report systematic improvements for their robustified versions with two distinct datasets.

    Original languageEnglish
    Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
    Number of pages7
    ISBN (Electronic)9781509046331
    Publication statusPublished - 21 Jul 2017
    Publication typeA4 Article in conference proceedings
    EventIEEE International Conference on Robotics and Automation -
    Duration: 1 Jan 19001 Jan 2000


    ConferenceIEEE International Conference on Robotics and Automation

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Software
    • Artificial Intelligence
    • Electrical and Electronic Engineering


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