基于正交多项式的平原城区机载LiDAR数据滤波算法

从机载LiDAR点云数据中分离地面点与非地面点生成城区DEM,是构建数字城市的首要工作。该文对机载LiDAR数据处理中的滤波关键算法进行了研究。首先,利用常用的区域增长方法对机载LiDAR点云数据进行了滤波处理,然后基于正交多项式分带滤波方法进行了点云滤波,比较了两种算法的运行效率,分析了二者的特点,并通过试验对比进行了阈值参数优选,试验表明该算法具有较好的滤波效果,在平原城区的数字城市三维建模中具有很好的应用价值。

Orthogonal Polynomial Filtering Algorithm for Plain Urban Airborne LiDAR Data

Airborne LiDAR data are a powerful data source for acquisition and update of high resolution topographic information.Current airborne LiDAR systems are able to acquire densities of up to 40 points per square meter so as to build up accurate digital city models.With such information topographic,objects like buildings,roads,trees,electricity lines,and the relief can be determined.The main problem is the classification and identification of these objects from the airborne LiDAR point cloud and precise reconstruction of city models.Extraction ground and non-ground points from airborne LiDAR data to generate DEM is a primary task for building the digital city.In this paper,the problem of filtering laser scanner point clouds would be tackled.This paper mainly presents the airborne LiDAR data filtering algorithms.First of all,use the region growth algorithm to filtering point cloud data;then the orthogonal polynomial filtering algorithm is employed to filter the point clouds,compare the efficiency of these two algorithms,analyses their characteristics.Then take the characteristics of the surveyed area into consideration to determine the optimal filtering threshold based on the compared experiment results.In the paper,these steps are described in detail.The experiment results prove that this research has a certain application value for the plain urban three-dimensional digital city modeling.