一种改进的激光点云滤波算法  

Improved Laser Point Cloud Filtering Algorithm

作　　者：韩浩宇 张元[1] 韩燮[1] Han Haoyu;Zhang Yuan;Han Xie(College of Big Data,North University of China,Taiyuan,Shanxi 030051,China)

机构地区：[1]中北大学大数据学院,山西太原030051

出　　处：《激光与光电子学进展》2021年第20期85-91,共7页Laser & Optoelectronics Progress

基　　金：国家重点研发计划(2018YFB2101504);山西省重点研发计划(201803D121081,201903D121147);山西省自然科学基金(201901D111150)。

摘　　要：针对常规的点云滤波方法在去除接近模型噪声的过程中会对模型造成较大破坏的问题,提出一种结合双张量投票和多尺度法向量估计的点云滤波算法。首先采用主成分分析法在较大的尺度下估计各点的法向量,对各点进行双张量投票以提取特征点。然后对提取出的特征点在较小的尺度下估计法向量,并结合随机采样一致性方法对小范围噪声平面进行剔除。最后采用曲率对剩余的噪声进行滤波,获得最终的点云数据。实验结果表明,所提算法可以有效剔除噪声点,并较好地保留三维模型的尖锐特征,为后续点云配准和三维重建奠定基础。Aiming at the problem that the conventional point cloud filtering method will cause greater damage to the model in the process of removing the noise close to the model,apoint cloud filtering algorithm combining dual tensor voting and multi-scale normal vector estimation is proposed.First,the principal component analysis method is used to estimate the normal vector of each point on a larger scale,and the double tensor voting is performed on each point to extract the feature points.Then,the normal vectors of the extracted feature points are estimated at a smaller scale,and the small-scale noise plane is eliminated by combining the random sample consensus method.Finally,the curvature is used to filter the remaining noise to obtain the final point cloud data.Experimental results show that the proposed algorithm can effectively eliminate noise points,and better retain the sharp features of the 3D model,which lays the foundation for subsequent point cloud registration and 3 Dreconstruction.

关 键 词：图像处理 点云滤波 张量投票 随机采样一致性 多尺度法向量估计 曲率 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]