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基于旋转差值核估计的激光雷达点云建筑物边缘提取

Building Edge Extraction from LiDAR Point Cloud Based on Rotational Difference Kernel Estimation

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摘要

提出了一种基于旋转差值核估计的激光雷达(LiDAR)点云边缘提取方法。对点云中任意数据点, 在给定方向上以该数据点为对称中心, 以一定间距构建对称窗口; 在对称窗口中定义距离核函数, 计算两窗口内数据点的高程加权均值, 将两加权均值之差的绝对值作为该数据点在该方向上的边缘强度, 并选取所有方向上的最大边缘强度作为边缘点判据。计算最大边缘强度对应方向上两窗口内数据点的高程方差, 结合两方差的差值绝对值和边缘点判据提取建筑物与地面交界点; 调整两窗口间距, 再次计算所有方向上最大的高程方差之差绝对值, 并将该绝对值作为树木点的判据, 并在依此判据检测出的点集上去除建筑物与地面交界点后提取出树木点。利用激光传播特性将点云数据中的树木点滤除后, 再提取完整的建筑物边缘。实验结果表明, 所提方法有效克服了树木的影响, 建筑物边缘的提取精度约为80%。

Abstract

An edge extraction method from LiDAR point cloud data based on rotation difference kernel estimation is proposed. For any point in the point cloud, the symmetrical center is the data point in a given direction, and the symmetrical window is constructed with a certain distance.The Kernel function about distance is defined in symmetric windows, and the weighted mean of elevations for the data points within the two windows is calculated. The absolute value of difference between the two weighted mean values is employed as edge magnitude of data point in the direction, and the maximum edge magnitude in all directions is selected as criterion for edge points. Then variances of elevations for the data points within the two windows in the direction corresponding to maximum edge magnitude is calculated, and the boundary points between buildings and ground are extracted by combining the absolute value of the difference between the two variances and the criterion of the edge points. By adjusting the distance between two windows, the maximum absolute value of the difference between the elevation variance in all directions is obtained, and this absolute value is used as the criterion of tree points. The absolute value of the difference between the two variances is used as the criterion of tree points, and the tree points are extracted after removing the junction between the building and the ground from the set of points detected by the criterion. The tree points in point cloud data are filtered by laser propagation characteristics, and then the complete building edges are extracted. The experimental results show that the proposed method effectively overcomes the influence of trees, and the accuracy of building edge extraction is about 80%.

Newport宣传-MKS新实验室计划
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中图分类号:P237

DOI:10.3788/cjl201946.0104005

所属栏目:测量与计量

基金项目:国家自然科学基金(41271435, 41301479)、辽宁省自然科学基金(2015020090)、辽宁工程技术大学测绘与地理科学学院研究生优秀学位论文培育计划项目(YS201606)

收稿日期:2018-09-20

修改稿日期:2018-10-08

网络出版日期:2018-10-09

作者单位    点击查看

王岱良:辽宁工程技术大学测绘与地理科学学院遥感科学与应用研究所, 辽宁 阜新 123000
李玉:辽宁工程技术大学测绘与地理科学学院遥感科学与应用研究所, 辽宁 阜新 123000

联系人作者:王岱良(bengbeng4545@126.com)

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引用该论文

Wang Dailiang,Li Yu. Building Edge Extraction from LiDAR Point Cloud Based on Rotational Difference Kernel Estimation[J]. Chinese Journal of Lasers, 2019, 46(1): 0104005

王岱良,李玉. 基于旋转差值核估计的激光雷达点云建筑物边缘提取[J]. 中国激光, 2019, 46(1): 0104005

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