郑州大学学报（工学版）2016，Vol.37Issue(1)：92-96,5.DOI:10.3969/j.issn.1671-6833.201504004
LIDAR点云数据全自动滤波算法研究
An Automatic Point Clouds Filtering Algorithm Based on Grid Partition and Simplified Moving Least Squares
摘要
Abstract
An automatic point clouds filtering algorithm is presented on the basis of Grid Partition using Dy-namic Quad Trees and reference surface fitted by Moving Least Squares. The filtering processing contains three major steps:Firstly, it gives the LIDAR point clouds reasonable grid partitions and establishes the correspond-ing dynamic quad trees spatial indices. Secondly, the points in the partitioned grids are utilized to fit a DEM reference plane using moving least squares technology. Finally, the elevation threshold is setup to separate ground points from those non-ground ones who are positioned above the reference plane and have a distance ex-ceeding the threshold value to the plane. The aforementioned steps have to be repeated on the obtained ground points with gradually decreasing thresholds and grid size until desired precision is achieved. The experiments show that simplified moving least squares is high efficiency, small amount of calculation and high precision DEM data for point cloud fitting, and the filtering algorithm has high precision and can effectively identify ground points and non-ground ones without losing the detailed information of topography.关键词
点云数据/数字地面模型/滤波算法/动态四叉树/移动最小二乘法Key words
point clouds data/DEM/filtering algorithm/dynamic quad trees/moving least square分类
天文与地球科学引用本文复制引用
李健,方宏远,崔雅博,范涛..LIDAR点云数据全自动滤波算法研究[J].郑州大学学报（工学版）,2016,37(1):92-96,5.基金项目
国家自然科学基金青年基金资助项目(41404096) （41404096）
河南省教育厅基金资助项目(14A420002,15A420002) （14A420002,15A420002）
