为提高机载LiDAR点云的滤波精度,更好地服务于高精度数字高程模型(Digital Elevation Model,DEM)制作,本文综合各种机载LiDAR点云滤波方法的优点,提出一种组合点云滤波方法.该组合滤波算法由渐进三角网加密(Progressive TIN Densification,PTD)滤波、邻域高差滤波以及平面拟合滤波算法组成.首先,使用PTD算法对原始机载LiDAR点云数据进行初始滤波,滤除大部分地物点;其次,使用邻域高差滤波算法补全缺失点云区域;最后,使用平面拟合滤波算法滤除近地噪声点.本文提出的组合滤波算法综合了上述 3 种滤波算法的优势,提高了对地形的自适应性以及近地点去噪的处理能力,实验表明本文提出的组合滤波算法能有效弥补单一PTD滤波算法难以保留陡坡区域地形特征的缺陷,有效提升点云滤波的质量.
Research on Combined Filtering Method Based on Airborne LiDAR Point Cloud Data
In order to improve the accuracy of airborne LiDAR point cloud filtering and better serve the production of high-accuracy digital elevation model(DEM),this paper integrates the advantages of various airborne LiDAR point cloud filtering methods and pro-poses a combined point cloud filtering method.The combined filtering algorithm is composed of Progressive TIN Densification(PTD)filtering,neighborhood height difference filtering and plane fitting filtering algorithms.First,the PTD algorithm is used to perform ini-tial filtering on the original airborne LiDAR point cloud data to remove most of the ground objects;secondly,the neighborhood height difference filtering algorithm is used to complete the missing point cloud region;finally,the plane fitting filtering algorithm is used to filter out the near-ground noise points.This paper proposes a combined filtering algorithm that integrates the advantages of the above three filtering algorithms,which improves the adaptability to terrain and the processing ability of near-ground denoising.The experi-ment also shows that the combined filtering algorithm in this paper can effectively improve the shortcoming that a single PTD filtering algorithm is difficult to retain the terrain features of steep slope areas,and effectively improve the quality of point cloud filtering.
airborne LiDAR point cloud filteringprogressive TIN densificationneighborhood height difference filteringplane fitting filtering
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