一种机载LiDAR点云自适应坡度阈值去植被算法
An Adaptive Slope Threshold Airborne LiDAR Point Cloud Devegetation Algorithm

针对利用机载LiDAR点云数据制作高精度DEM时易受地形及茂密植被干扰问题，提出一种改进的基于坡度和地形的滤波算法：新算法按一定尺度将点云格网化后，引入地形判断因子，判断格网内地形并自适应调整格网的坡度阈值，从而避免过渡平滑或忽略较小地形特征导致的地形失真，以及在陡坡断崖处滤波效果不佳的问题。对FARO SINGAPORE PTE. LTD公司提供的机载LiDAR点云数据处理结果表明，相对于经典坡度滤波本文算法优势明显，在地形复杂且植被密度大的林区也能很好地保留地面点并滤除植被等非地面点，得到真实的地形信息。在陡坡断崖地形区域，本文算法总误差较布料模拟算法降低了2.68%，滤波效果较好。
Aiming at the problem that the use of airborne LiDAR point cloud data to produce high-precision DEM is susceptible to terrain and dense vegetation interference, an improved filtering algorithm based on slope and terrain is proposed. After the point cloud is grid based on a certain scale, the terrain judgment factor is introduced to judge the terrain in the grid and adaptively adjust the gradient threshold of the grid, so as to avoid the problem of terrain distortion caused by smooth transition or ignoring small terrain features, and poor filtering effect on steep slopes and cliffs. The processing results of the airborne LiDAR point cloud data provided by FARO SINGAPORE PTE. LTD show that the algorithm in this paper has obvious advantages compared with the classical slope fil-tering. In the forest area with complex terrain and high vegetation density, the ground points can be well preserved and filtered out. Non-ground points such as vegetation can get real terrain infor-mation. In the area of steep slope and cliff terrain, the total error of the algorithm in this paper is reduced by 2.68% compared with the cloth simulation algorithm, and the filtering effect is better.