Matching point pair optimization registration method for point cloud model

YU Yongwei; WANG Kang; DU Liuqing; QU Bing

doi:10.37188/OPE.20233104.0503

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Matching point pair optimization registration method for point cloud model

Information Sciences | 更新时间：2023-02-28

- Matching point pair optimization registration method for point cloud model
- Optics and Precision Engineering Vol. 31, Issue 4, Pages: 503-516(2023)
- 作者机构：
  
  重庆理工大学机械工程学院，重庆 400054
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233104.0503
  CLC： TP394.1;TH691.9
- Received：15 August 2022，
  
  Revised：29 August 2022，
  
  Published：25 February 2023
- 稿件说明：
移动端阅览
余永维,王康,杜柳青等.点云模型的匹配点对优化配准[J].光学精密工程,2023,31(04):503-516.

YU Yongwei,WANG Kang,DU Liuqing,et al.Matching point pair optimization registration method for point cloud model[J].Optics and Precision Engineering,2023,31(04):503-516.
余永维,王康,杜柳青等.点云模型的匹配点对优化配准[J].光学精密工程,2023,31(04):503-516. DOI： 10.37188/OPE.20233104.0503.

YU Yongwei,WANG Kang,DU Liuqing,et al.Matching point pair optimization registration method for point cloud model[J].Optics and Precision Engineering,2023,31(04):503-516. DOI： 10.37188/OPE.20233104.0503.

摘要

针对传统迭代最近点（ICP）算法在点云存在重叠或部分重叠时，配准误差大且适应性差的问题，提出一种基于匹配点对加权优化的改进配准算法。首先，提出一种改进体素降采样算法对点云进行采样，减少数据量的同时提高算法对噪声的鲁棒性；然后，采用改进Sigmoid函数赋予参与配准的匹配点对不同的权重，克服传统算法忽视距离较小的匹配点对中仍具有错误点对的缺点，同时提高了配准精度和收敛速度；最后，提出一种采用奇异值分解法（SVD）求解配准参数的方法，进一步提高配准精度。设计了不同重叠度的配准实验和噪声实验，并结合曲轴三维点云重建对本文算法进行验证。实验结果表明：本文算法误差较Tr-ICP算法减少了约34.1%，较AA-ICP算法减少了约29%，配准时间较Tr-ICP算法缩短了约16.1%。最终表明本文算法具有更高配准精度的同时，具有更好的适用性和鲁棒性。

Abstract

To address the problems of large registration errors and poor adaptability of the traditional iterative closest point （ICP） algorithm when point clouds overlap or partially overlap， an improved registration algorithm based on weighted optimization of matching point pairs is proposed. First， an improved voxel downsampling algorithm is proposed to sample point clouds， which reduces the amount of data and improves the robustness of the algorithm against noise. Then， the improved Sigmoid function is used to assign different weights to the matching point pairs participating in the registration， which overcomes the disadvantage of traditional algorithms that ignore matching point pairs with small distances still have wrong point pairs， while improves the registration accuracy and convergence speed. Finally， a method to solve registration parameters using singular value decomposition （SVD） is proposed to further improve registration accuracy. The registration and noise experiments with different overlapping degrees were performed， and the proposed algorithm was verified by combining the three-dimensional point cloud reconstruction of the crankshaft. The experimental results showed that， compared with the Tr-ICP and AA-ICP algorithms， the error in the proposed algorithm was reduced by approximately 34.1% and 29%， respectively. Further， the registration time was shortened by approximately 16.1% compared with the Tr-ICP algorithm. Hence， compared with traditional algorithms， the proposed algorithm has higher registration accuracy， better applicability， and robustness.

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