一种基于分割的机载LiDAR点云数据滤波

针对当前滤波算法在处理地形不连续区域或存在复杂建筑物区域时容易过分＂腐蚀＂地形并难以去除一些低矮植被的不足,提出了一种基于分割的机载LiDAR点云滤波算法。首先,对原始点云基于地表连续性进行分割;然后,在移除点数目较小的粗差点集之后采用对分割点集建立缓冲区的方法,区分地面和非地面点集;在较大地物经过迭代分割基本移除之后,使用约束平面的方法移除高度较小的地表附着物以实现滤波。实验结果表明,与经典滤波算法相比,该算法提高了地面点的分类精度,在滤除地物信息的同时能有效地保留地形特征。

Filtering of LiDAR Based on Segmentation

In dealing with the problem that most current filtering algorithms excessively erode the bare earth at discontinuities or place with complex buildings,we present a new filtering algorithm based on segmentation.Firstly the original point cloud is segmented into many segments based on the continuity of terrain surface.Then,the ground point set is differed from non-ground point set using the way of establishing buffer area after gross error points remove by the number of segment.Finally,we get the filtering effect after several iterations.The experimental results show that this algorithm significantly improves the classification accuracy of ground points compared with other classical filters.This algorithm effectively preserves the ground information when filter kinds of object information.In addition comparing with the filtering effect of Terrascan software,the filtering effect of the proposed algorithm is better in removing vegetation and preserving the point in the edge of ground.