IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0197542.html
   My bibliography  Save this article

Robust 3D point cloud registration based on bidirectional Maximum Correntropy Criterion

Author

Listed:
  • Xuetao Zhang
  • Libo Jian
  • Meifeng Xu

Abstract

This paper presents a robust 3D point cloud registration algorithm based on bidirectional Maximum Correntropy Criterion (MCC). Comparing with traditional registration algorithm based on the mean square error (MSE), using the MCC is superior in dealing with complex registration problem with non-Gaussian noise and large outliers. Since the MCC is considered as a probability measure which weights the corresponding points for registration, the noisy points are penalized. Moreover, we propose to use bidirectional measures which can maximum the overlapping parts and avoid the registration result being trapped into a local minimum. Both of these strategies can better apply the information theory method to the point cloud registration problem, making the algorithm more robust. In the process of implementation, we integrate the fixed-point optimization technique based on the iterative closest point algorithm, resulting in the correspondence and transformation parameters that are solved iteratively. The comparison experiments under noisy conditions with related algorithms have demonstrated good performance of the proposed algorithm.

Suggested Citation

  • Xuetao Zhang & Libo Jian & Meifeng Xu, 2018. "Robust 3D point cloud registration based on bidirectional Maximum Correntropy Criterion," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
  • Handle: RePEc:plo:pone00:0197542
    DOI: 10.1371/journal.pone.0197542
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197542
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0197542&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0197542?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zhiyong Zhou & Jian Zheng & Yakang Dai & Zhe Zhou & Shi Chen, 2014. "Robust Non-Rigid Point Set Registration Using Student's-t Mixture Model," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    2. Tomas Petricek & Tomas Svoboda, 2017. "Point cloud registration from local feature correspondences—Evaluation on challenging datasets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    3. Lei Peng & Guangyao Li & Mang Xiao & Li Xie, 2016. "Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    4. Shaoyi Du & Yiting Xu & Teng Wan & Huaizhong Hu & Sirui Zhang & Guanglin Xu & Xuetao Zhang, 2017. "Robust iterative closest point algorithm based on global reference point for rotation invariant registration," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-14, November.
    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.
    1. Łukasz Marchel & Cezary Specht & Mariusz Specht, 2020. "Testing the Accuracy of the Modified ICP Algorithm with Multimodal Weighting Factors," Energies, MDPI, vol. 13(22), pages 1-17, November.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0197542. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.