基于小波变换的精密测量点云多尺度分解  

Multi-scale decomposition of point cloud data based on wavelet transform

作　　者：高凯元 刘雷[1] 崔海华[1] 李鹏程[1] 刘晓旭[2] 刘林 GAO Kaiyuan;LIU Lei;CUI Haihua;LI Pengcheng;LIU Xiaoxu;LIU Lin(College of Mechanical&Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Beijing Institute of Aerospace Metrology and Testing Technology,Beijing 100076,China)

机构地区：[1]南京航空航天大学机电学院,江苏南京210016 [2]北京航天计量测试技术研究所,北京100076

出　　处：《光学精密工程》2023年第3期340-351,共12页Optics and Precision Engineering

基　　金：江苏省自然科学基金资助项目(No.BK20191280,No.BK20210299);国家自然科学基金资助项目(No.52075238);航空科学基金资助项目(No.2020Z050052002)。

摘　　要：为了减小多源跨尺度点云数据尺度与数据量上的差异,提出了一种基于小波变换的点云多尺度分解方法。对细节丰富的小尺度点云数据进行多尺度分解,以及尺度分解在跨尺度点云数据配准中的应用进行研究。首先,对小尺度点云进行栅格建模,建立全局点云二值表达函数。根据离散小波变换理论,对栅格点云进行多次的三维小波分解,利用小波尺度函数的低通特性,保留低频信息来获取原始小尺度点云的近似尺度数据。然后,基于面维数和差体维数差表征与原始数据的相似度,确定有效的小波分解级数。最后,将各级分解得到的点云数据与大尺度点云数据进行精确配准,并将配准关系应用于原始点云,提高跨尺度点云的配准精度。实验结果表明:本文提出的多尺度分解方法能够对数据进行有效分解,应用于某航空发动机叶片多尺度测量中,将显微测量的局部气膜孔小尺度点云数据与整体叶片结构光数据配准,配准精度提升了61.36%。该分解方法应用于叶片边缘与栅格零件多尺度测量中,配准精度分别提升了48.59%,43.86%。所提的点云多尺度分解方法能够有效分解小尺度点云数据,大幅提升跨尺度数据的配准精度。To reduce the differences between the data scales and volume of multi-source cross-scale point cloud data,this study proposes a multi-scale decomposition method of point cloud data based on wavelet transform.This study examines the multi-scale decomposition of small-scale point cloud data with considerable attention and the application of scale decomposition in cross-scale point cloud data registration.First,the small-scale point cloud is grid modeled,and the global point cloud binary expression function is established.Subsequently,according to the theory of discrete wavelet transformation,three-dimensional wavelet decomposition of the grid point cloud is performed several times,and the low-pass characteristics of the wavelet scale function are used to retain the low-frequency information to obtain the approximate scale data of the original small-scale point cloud.The similarity with the original data is then characterized based on the surface dimension and the difference in body dimension,and the effective wavelet decomposition series is determined.Finally,the point cloud data obtained by decomposition at various levels are accurately registered with the large-scale point cloud data,and the registration relationship is applied to the original point cloud to increase the registration accuracy of the cross-scale point cloud data.The experimental results show that the multi-scale decomposition method proposed in this paper can effectively decompose the data.When applied to the multi-scale measurement of an aero-engine blade,the registration accuracy of the local cooling holes small-scale point cloud data and the overall blade structure light data of micrometry increased by 61.36%.The proposed decomposition method is applied to the multi-scale measurement of blade edge and grid parts,and the registration accuracy is increased by 48.59%and 43.86%,respectively.The proposed multi-scale decomposition method of the point cloud can effectively decompose small-scale point cloud data,and ultimately improve the registration

关 键 词：多尺度分解 小波变换 体维数 面维数 点云配准 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]