基于密度聚类的点云滤波算法研究

Point Cloud Filtering Algorithm Based on Density Clustering

根据激光雷达点云的特征属性,用聚类的方式进行滤波,虽然是一种比较实用的方法,但在实践中,因为点云的数据量巨大,直接利用点的三维坐标进行聚类时的耗时过长、滤波结果误差过大,而且现有的许多滤波算法在不连续地形处的表现不佳。为解决大型点云的直接聚类问题并保留不连续地形的整体起伏,提出了一种新的基于密度聚类的点云滤波算法。以激光雷达点云的空间密度、地物类点云及地形类点云的特征属性为依据,首先根据点云的高程值密度聚类,再进行平面点云的筛选,从而降低数据的样本数量,最后通过基于密度的噪声应用空间聚类算法进行聚类,将原始点云分为噪音类、地物类及地形类点云。采用国际摄影测量与遥感学会提供的数据样本进行实验,并将所提算法与其他8种经典滤波算法进行了比较。定量与定性结果表明,所提算法在城区和农村地区均有较好的适用性,在不连续地形处滤波误差较小,在人工建筑和植被混合地区适应性较好。所提算法具有可行性,可在不同地形中使用。

Clustering filtering is a practical method according to the characteristic attributes of the lidar point cloud.However, because of the large data size of the point cloud, direct clustering using three-dimensional point coordinates is time-consuming, produces large filtering error results, and existing filtering algorithms do not perform well in discontinuous terrain. In this paper, we proposed a new point cloud filtering algorithm based on density clustering to solve the direct clustering problem of large-scale point clouds and preserve the overall fluctuation of discontinuous terrain. First,based on the spatial density of lidar point cloud, the characteristic attributes of both ground object and terrain point clouds cluster according to the elevation value density of point cloud, and then screen the plane point cloud, to reduce the number of samples of data. Finally, the original point cloud is divided into noise, nonground, and ground point clouds using density-based spatial clustering of applications with noise algorithm. The experiment is conducted with data samples provided by the international society for photogrammetry and remote sensing. Furthermore, we compared the proposed algorithm with eight other classical filtering algorithms. The quantitative and qualitative results show that the proposed algorithm has good applicability in urban and rural areas, with small filtering error in discontinuous terrain and good adaptability in the mixed area of artificial buildings and vegetation. The proposed algorithm is feasible and can be used in different terrain.