Point cloud registration using neighborhood point information description and matching

LIN Sen; ZHANG Qiang

doi:10.37188/OPE.20223008.0984

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Point cloud registration using neighborhood point information description and matching

Information Sciences | 更新时间：2026-09-11

- Point cloud registration using neighborhood point information description and matching
- Optics and Precision Engineering Vol. 30, Issue 8, Pages: 984-997(2022)
- 作者机构：
  
  1.沈阳理工大学自动化与电气工程学院，辽宁沈阳 110159
  2.辽宁工程技术大学电子与信息工程学院，辽宁葫芦岛 125105
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223008.0984
  CLC： TP391.9
- Received：17 May 2021，
  
  Revised：2021-06-24，
  
  Published：25 April 2022
- 稿件说明：
移动端阅览
林森,张强.应用邻域点信息描述与匹配的点云配准[J].光学精密工程,2022,30(08):984-997.

LIN Sen,ZHANG Qiang.Point cloud registration using neighborhood point information description and matching[J].Optics and Precision Engineering,2022,30(08):984-997.
林森,张强.应用邻域点信息描述与匹配的点云配准[J].光学精密工程,2022,30(08):984-997. DOI： 10.37188/OPE.20223008.0984.

LIN Sen,ZHANG Qiang.Point cloud registration using neighborhood point information description and matching[J].Optics and Precision Engineering,2022,30(08):984-997. DOI： 10.37188/OPE.20223008.0984.

摘要

点云配准是现代制造业中逆向工程、机器视觉等技术的重要组成部分，其效率和精度对获取的产品数据模型有重要影响。为提高3D物体点云配准的精度和效率，提出一种应用邻域点信息描述与匹配（Neighborhood point information Description and Matching， NDM）的点云配准方法。首先，在三个半径比例下根据点的曲率变化、测量角度和特征值性质提取特征点；其次，计算改进的法向量夹角、点密度和曲率值，获取多尺度矩阵描述符；然后，为描述符建立k维树获取匹配关系，并提出几何特征约束和刚性距离约束组合，剔除错误点对，实现粗配准；最后，通过k维树改进迭代最近点（Iterative Closest Point， ICP）算法完成精确配准。本文设计了实际物体点云配准和斯坦福模型模拟真实物体配准两组实验。结果表明，本文算法解决了经典ICP的局限性，配准精度提高2~5个量级；相较于其他算法，实物点云配准中本文算法的配准精度至少提高29%，效率可提高54%；斯坦福模拟实验中，本文算法的配准精度提高1%~99%，配准耗时降低3%~94%，表明本文算法是一种有效的物体表面点云的配准方法，可以提高配准精度和效率，有较好的鲁棒性。

Abstract

Point cloud registration is an important part of reverse engineering， machine vision and other technologies in modern manufacturing. Its efficiency and accuracy have an important impact on the acquisition of product data model. In order to improve the accuracy and efficiency of 3D object point cloud registration， a point cloud registration method using Neighborhood point information Description and Matching （NDM） is proposed. Firstly， under three radius ratios， feature points are extracted according to the change of curvature， measurement angle and eigenvalue property； secondly， the improved normal vector angle， point density and curvature are calculated to obtain multi-scale matrix descriptor； then， a k-dimensional tree is established for descriptors， and the matching relationship is preliminarily established. The combination of geometric feature constraint and rigid distance constraint is proposed to eliminate the wrong points； finally， the k-tree improved iterative closest point （ICP） algorithm is used to complete the accurate registration. In this paper， two groups of experiments are designed， which are real object point cloud registration and Stanford model simulation real object registration. The results show that the algorithm solves the limitations of the classical ICP， and improving the registration accuracy by 2

5 orders of magnitude. Compared with other algorithms， the registration accuracy of this algorithm is improved by at least 29%， and the efficiency is increased by 58%； in the Stanford database simulation experiment， the registration accuracy is improved by 1%

99%， and the registration time is reduced by 3%

94%. It is proved that this algorithm is an effective registration method of point cloud on object surface， which can

improve the registration accuracy and efficiency， and has good robustness.

关键词

Keywords

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