Quantitative evaluation method for structural similarity of multidimensional point cloud

YANG Ziqian; WANG Yanqiu; ZHENG Fu; SUN Zhibin

doi:10.37188/OPE.20233104.0533

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Quantitative evaluation method for structural similarity of multidimensional point cloud

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

- Quantitative evaluation method for structural similarity of multidimensional point cloud
- Optics and Precision Engineering Vol. 31, Issue 4, Pages: 533-542(2023)
- 作者机构：
  
  1.中国科学院国家空间科学中心，北京 100190
  2.中国科学院大学，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233104.0533
  CLC： TP753
- Received：23 February 2022，
  
  Revised：31 March 2022，
  
  Published：25 February 2023
- 稿件说明：
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杨紫骞,王艳秋,郑福等.多维点云结构相似性定量化评价[J].光学精密工程,2023,31(04):533-542.

YANG Ziqian,WANG Yanqiu,ZHENG Fu,et al.Quantitative evaluation method for structural similarity of multidimensional point cloud[J].Optics and Precision Engineering,2023,31(04):533-542.
杨紫骞,王艳秋,郑福等.多维点云结构相似性定量化评价[J].光学精密工程,2023,31(04):533-542. DOI： 10.37188/OPE.20233104.0533.

YANG Ziqian,WANG Yanqiu,ZHENG Fu,et al.Quantitative evaluation method for structural similarity of multidimensional point cloud[J].Optics and Precision Engineering,2023,31(04):533-542. DOI： 10.37188/OPE.20233104.0533.

摘要

点云配准技术作为点云数据处理中的核心技术，会因为点云质量而影响配准效果，质量好的点云可以提高配准精度、空间模型完整度、即时定位与地图构建系统的性能等，因此评价点云数据质量具有很高的客观价值；而通过传感器获得的点云数据存在系统误差和非系统误差等噪声，因此点云数据处理变得很重要，对点云数据处理效果的评价还没有一个较为客观的方法，本文提出了多维点云结构相似性定量化评价方法。通过将滤波前、后的点云数据和标准点云数据进行对比，分别比较三维坐标轴上所有点坐标的均值、标准差和协方差，再对3个坐标轴上的结构相似性值进行权重分配最终获得三维结构相似关联度，进而对点云滤波、点云稀疏后的点云数据质量进行评价。该方法还对提高配准精度进行了实验验证。经过实验表明，该方法能够评价三维点云的质量，且能够评价不同噪声类型以及处理方法下获得的点云质量，为点云配准提供了参考。该方法不仅对点云预处理和点云质量进行了高效、客观地评价，还提供了一种提高点云配准精度和效率的方法。

Abstract

Point cloud registration technology is the core technology of point cloud data processing. The quality of the point cloud will influence the registration effect of point cloud registration. An excellent quality point cloud can improve registration accuracy， spatial integrity， and slam performance. Therefore， assessing the quality of point cloud data has significant objective value. The point cloud data obtained by the sensor contains noise such as systematic and nonsystematic errors. In this case， point cloud data processing becomes crucial. However， there is no more objective method to evaluate the treatment effect. A quantitative evaluation method of multidimensional point cloud structure similarity is proposed. This method compares the point cloud data before and after filtering with the standard data. The mean， standard deviation， and covariance of all point coordinates on the three-dimensional coordinate axis are compared. Subsequently， the structural similarity values on the three coordinate axes are weighted. Finally， the similarity and correlation degree of the three-dimensional structure is obtained. Then， it realizes the evaluation of point cloud filtering， point cloud sparse， and point cloud data quality. The method is also verified by experiments to improve registration accuracy. Experiments demonstrate its capacity to evaluate the quality of the 3D point cloud. It can evaluate the quality of the point cloud obtained under different noise types and processing methods. It provides a reference for point cloud registration. This method improves both the accuracy and efficiency of cloud point registration， as well as its quality.

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references

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