IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p35-d1011161.html
   My bibliography  Save this article

Coarse Point Cloud Registration Based on Variational Functionals

Author

Listed:
  • Artyom Makovetskii

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia)

  • Sergei Voronin

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia)

  • Vitaly Kober

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
    Department of Computer Science, Ensenada Center for Scientific Research and Higher Education, Ensenada 22860, BC, Mexico)

  • Alexei Voronin

    (Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia)

Abstract

Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point cloud pairs consequentially using a variant of the iterative closest point (ICP) algorithm, but most versions of the ICP algorithm only work correctly for a small movement between two point clouds. This makes it difficult to accumulate multiple scans. Global registration algorithms are also known, which theoretically process point clouds at arbitrary initial positions. Recently, a multiparameter variational functional was described and used in the ICP variant to register point clouds at arbitrary initial positions. The disadvantage of this algorithm was the need for manual selection of parameters. In this paper, a modified version of the algorithm with automatic selection of the model parameters is proposed. The proposed algorithm is a fusion of the ICP and RANSAC concepts. Moreover, the algorithm can be parallelized. The performance of the proposed algorithm is compared with that of known global registration algorithms.

Suggested Citation

  • Artyom Makovetskii & Sergei Voronin & Vitaly Kober & Alexei Voronin, 2022. "Coarse Point Cloud Registration Based on Variational Functionals," Mathematics, MDPI, vol. 11(1), pages 1-25, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:35-:d:1011161
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/35/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/1/35/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Artyom Makovetskii & Sergei Voronin & Vitaly Kober & Aleksei Voronin, 2021. "Point Cloud Registration Based on Multiparameter Functional," Mathematics, MDPI, vol. 9(20), pages 1-20, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:35-:d:1011161. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.