基于机载LiDAR的点云数据滤波方法  

Filtering Methods of Point Cloud Data Based on Airborne LiDAR

作　　者：黄代军 HUANG Daijun(Zhejiang Institute of Surveying and Mapping Science and Technology,Hangzhou,Zhejiang,310030,China)

机构地区：[1]浙江省测绘科学技术研究院,浙江杭州310030

出　　处：《测绘标准化》2022年第1期30-35,共6页Standardization of Surveying and Mapping

摘　　要：为了对机载LiDAR点云数据进行有效滤波,减小常用滤波方法产生的一类误差和二类误差,提出一种基于偏度平衡的移动曲面拟合滤波算法。该算法在确定种子点的过程中加入偏度概念,以便有效降低种子点误差,可在上一次滤波完成后继续对非地面点与地面点进行滤波,从而实现对地面点与非地面点的有效分离,提高滤波精度。同时,使用国际摄影测量与遥感学会提供的数据与移动曲面拟合算法得到的数据进行对比试验。结果表明,基于偏度平衡的移动曲面拟合算法可有效减小一类误差和二类误差,提高滤波精度与效率。基于偏度的移动曲面拟合滤波算法适用于城市地区点云滤波,能取得较为可靠的滤波效果和更高的滤波精度。In order to filter airborne LiDAR point cloud data effectively and reduce the class I and class II errors caused by common filtering methods,a moving surface fitting filtering algorithm based on skewness balance is proposed.In the algorithm,the concept of skewness is added in the process of determining seed points in order to reduce the error of seed points effectively,and the off ground points and ground points can be filtered after the last filtering,so as to achieve effective separation of ground points and off ground points and improve the filtering accuracy.At the same time,the data provided by the International Society of Photogrammetry and Remote Sensing are compared with the data of moving surface fitting algorithm.The results show that the moving surface fitting algorithm based on skewness balance can effectively reduce the class I and class II errors and improve the filtering accuracy and efficiency.The moving surface fitting filtering algorithm based on skewness is suitable for point cloud filtering in urban areas,and which can achieve more reliable filtering effect and higher filtering accuracy.

关 键 词：机载激光扫描技术 点云数据 滤波 移动曲面拟合 偏度 

分 类 号：TN713[电子电信—电路与系统] P237[天文地球—摄影测量与遥感]