APPLIED LASER, Volume. 45, Issue 5, 168(2025)

Point Cloud Registration Based on Local Curvature and Distance Characteristics

Zhu Zhenyu1, Ding Haiyong1、*, and Liu Chunlei2
Author Affiliations
  • 1School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
  • 2Nanjing Longce Measurement Technology Co., Ltd., Nanjing 210031, Jiangsu, China
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    References(4)

    [1] [1] SERAFIN J, GRISETTI G. Using extended measurements and scene merging for efficient and robust point cloud registration[J]. Robotics and Autonomous Systems, 2017, 92: 91-106.

    [9] [9] KLEPPE A L, TINGELSTAD L, EGELAND O. Coarse alignment for model fitting of point clouds using a curvature-based descriptor[J]. IEEE Transactions on Automation Science and Engineering, 16(2): 811-824.

    [14] [14] THEILER P W, WEGNER J D, SCHINDLER K.Markerless point cloud registration with keypoint-based 4-points congruent sets[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, II5: 283-288.

    [16] [16] BESL P J, MCKAY H D. A method for registration of 3D shapes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1992, 14(2):239-256.

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    Zhu Zhenyu, Ding Haiyong, Liu Chunlei. Point Cloud Registration Based on Local Curvature and Distance Characteristics[J]. APPLIED LASER, 2025, 45(5): 168

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    Paper Information

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    Received: Sep. 28, 2023

    Accepted: Sep. 8, 2025

    Published Online: Sep. 8, 2025

    The Author Email: Ding Haiyong (hyongd@163.com)

    DOI:10.14128/j.cnki.al.20254504.168

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