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Research on 3D point cloud alignment algorithm based on SHOT features

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  • Zheng Fu
  • Enzhong Zhang
  • Ruiyang Sun
  • Jiaran Zang
  • Wei Zhang

Abstract

To overcome the problem of the high initial position of the point cloud required by the traditional Iterative Closest Point (ICP) algorithm, in this paper, we propose a point cloud registration method based on normal vector and directional histogram features (SHOT). Firstly, a hybrid filtering method based on the voxel idea is proposed and verified using the measured point cloud data, and the noise removal rates of 97.5%, 97.8%, and 93.8% are obtained. Secondly, in terms of feature point extraction, the original algorithm is optimized, and the optimized algorithm can better extract the missing part of the point cloud. Finally, a fine alignment method based on normal vector and directional histogram features (SHOT) is proposed, and the improved algorithm is compared with the existing algorithm. Taking the Stanford University point cloud data and the self-measured point cloud data as examples, the plotted iteration-error plots can be concluded that the improved method can reduce the number of iterations by 40.23% and 37.62%, respectively.

Suggested Citation

  • Zheng Fu & Enzhong Zhang & Ruiyang Sun & Jiaran Zang & Wei Zhang, 2024. "Research on 3D point cloud alignment algorithm based on SHOT features," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-16, March.
    • Handle: RePEc:plo:pone00:0296704
    DOI: 10.1371/journal.pone.0296704
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    References listed on IDEAS

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    1. Ju Zhang & David Ackland & Justin Fernandez, 2018. "Point-cloud registration using adaptive radial basis functions," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 21(7), pages 498-502, May.
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