引用本文: 隋立春, 张熠斌, 张硕, 陈卫. 基于渐进三角网的机载LiDAR点云数据滤波[J]. 武汉大学学报 ( 信息科学版), 2011, 36(10): 1159-1163.
SUI Lichun, ZHANG Yibin, ZHANG Shuo, CHEN Wei. Filtering of Airborne LiDAR Point Cloud Data Based on Progressive TIN[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1159-1163.
 Citation: SUI Lichun, ZHANG Yibin, ZHANG Shuo, CHEN Wei. Filtering of Airborne LiDAR Point Cloud Data Based on Progressive TIN[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1159-1163.

## Filtering of Airborne LiDAR Point Cloud Data Based on Progressive TIN

• 摘要: 机载LiDAR点云数据滤波是获取高精度数字高程模型的关键,也是目前LiDAR点云数据处理领域研究的重点和难点之一。提出了基于渐进三角网的机载LiDAR点云数据滤波方法,首先以规则格网和不规则三角网组织数据,采用区域分块法或数学形态学法选取种子地面点建立初始稀疏三角网,通过不断向上加密三角网提取地面点。试验结果表明,该算法能有效地滤除不同尺寸的建筑物、低矮的植被和其他地物,地形特征保持较好。最后选取了不同区域的点云数据进行了滤波试验和算法验证。

Abstract: Airborne LiDAR point cloud data filtering is not only the key to obtain high-precision Digital Elevation Model,but also one of the difficulties and the focus in the current study of the LiDAR point cloud data post-processing.In this paper,the method is proposed for filtering LiDAR point cloud data based on progressive TIN and the details of filter principle is described.Firstly,LiDAR point cloud data is organized by regular grid and Triangulated Irregular Network(TIN),seed points are selected by regional sub-block method or mathematical morphology.Then,an initial sparse TIN is created from the seed points and densified upward and ground points are extracted in an interactive process.In experiments it is shown that the filter method can effectively remove different sizes of buildings,low vegetation and other objects,and keep topographical features better.Finally,the point cloud of different regions is selected to do the experiment,and the shaded relief maps for the grids generated from unfiltered and filtered LiDAR point cloud data are shown.

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