Object recognition and pose estimation based on combined use of projection histograms and surface fitting

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

    Abstract

    In this paper we present a configurable object recognition and locating system for 3D point cloud sensors. The objects are recognized based on cylindrical projection histograms and after the object is recognized, the initial pose of the object is computed based on the eigenvectors of the modelled and measured 3D point clusters. The optimal 6 degree of freedom pose is estimated by fitting the CAD-model surfaces to the measured 3D-points, where the model surfaces and 3D points are correlated based on the modelled and measured eigenvectors. The novelty of our system is the combination of reliable histogram based object recognition and accurate CAD-based pose estimation in the object recognition system with configurability options according to application requirements and point cloud properties.

    Original languageEnglish
    Title of host publication13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
    PublisherAmerican Society of Mechanical Engineers ASME
    Number of pages7
    ISBN (Print)978-0-7918-5823-3
    DOIs
    Publication statusPublished - 1 Jan 2017
    MoE publication typeA4 Article in a conference publication
    Event13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications - Cleveland, United States
    Duration: 6 Aug 20179 Aug 2017
    Conference number: 13

    Publication series

    SeriesASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
    Volume9

    Conference

    Conference13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
    Abbreviated titleIDETC/CIE 2017
    CountryUnited States
    CityCleveland
    Period6/08/179/08/17

    Fingerprint

    Surface Fitting
    Pose Estimation
    Object recognition
    Object Recognition
    Histogram
    Point Cloud
    Projection
    Eigenvalues and eigenfunctions
    Eigenvector
    Computer aided design
    Degree of freedom
    Sensor
    Requirements
    Sensors
    Model
    Object

    Keywords

    • object recognition
    • CAD models
    • geometric models

    Cite this

    Ahola, J., & Heikkilä, T. (2017). Object recognition and pose estimation based on combined use of projection histograms and surface fitting. In 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications [DETC2017-67315] American Society of Mechanical Engineers ASME. ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol.. 9 https://doi.org/10.1115/DETC2017-67315
    Ahola, Jari ; Heikkilä, Tapio. / Object recognition and pose estimation based on combined use of projection histograms and surface fitting. 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications. American Society of Mechanical Engineers ASME, 2017. (ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 9).
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    title = "Object recognition and pose estimation based on combined use of projection histograms and surface fitting",
    abstract = "In this paper we present a configurable object recognition and locating system for 3D point cloud sensors. The objects are recognized based on cylindrical projection histograms and after the object is recognized, the initial pose of the object is computed based on the eigenvectors of the modelled and measured 3D point clusters. The optimal 6 degree of freedom pose is estimated by fitting the CAD-model surfaces to the measured 3D-points, where the model surfaces and 3D points are correlated based on the modelled and measured eigenvectors. The novelty of our system is the combination of reliable histogram based object recognition and accurate CAD-based pose estimation in the object recognition system with configurability options according to application requirements and point cloud properties.",
    keywords = "object recognition, CAD models, geometric models",
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    Ahola, J & Heikkilä, T 2017, Object recognition and pose estimation based on combined use of projection histograms and surface fitting. in 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications., DETC2017-67315, American Society of Mechanical Engineers ASME, ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 9, 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Cleveland, United States, 6/08/17. https://doi.org/10.1115/DETC2017-67315

    Object recognition and pose estimation based on combined use of projection histograms and surface fitting. / Ahola, Jari; Heikkilä, Tapio.

    13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications. American Society of Mechanical Engineers ASME, 2017. DETC2017-67315 (ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 9).

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

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    Ahola J, Heikkilä T. Object recognition and pose estimation based on combined use of projection histograms and surface fitting. In 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications. American Society of Mechanical Engineers ASME. 2017. DETC2017-67315. (ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 9). https://doi.org/10.1115/DETC2017-67315