地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (11): 2118-2127.doi: 10.12082/dqxxkx.2020.190326

• 地球信息科学理论与方法 • 上一篇    下一篇

一种灰度体素结构分割模型下的机载LiDAR 3D滤波算法

王丽英*(), 赵元丁   

  1. 辽宁工程技术大学 测绘与地理科学学院,阜新 123000
  • 收稿日期:2019-06-24 修回日期:2019-10-18 出版日期:2020-11-25 发布日期:2021-01-25
  • 通讯作者: 王丽英 E-mail:wangliyinglntu@163.com
  • 作者简介:王丽英(1982— ),女,河北石家庄人,博士,教授,主要从事激光雷达数据处理及应用研究。E-mail: wangliyinglntu@163.com
  • 基金资助:
    辽宁省教育厅科学技术研究项目(LJ2019JL015)

An Airborne LiDAR 3D Filtering Algorithm based on the Grayscale Voxel Structure Segmentation Model

WANG Liying*(), ZHAO Yuanding   

  1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China
  • Received:2019-06-24 Revised:2019-10-18 Online:2020-11-25 Published:2021-01-25
  • Contact: WANG Liying E-mail:wangliyinglntu@163.com
  • Supported by:
    Scientific Research Fund of Liaoning Provincial Education Department(LJ2019JL015)

摘要:

针对现有的基于机载LiDAR数据的滤波算法未能充分利用数据提供的所有信息及其所采用的数据结构表达复杂、存在信息损失等缺陷,提出了一种灰度体素结构分割模型下的机载LiDAR 3D滤波算法。算法首先以综合利用机载LiDAR数据的高程及强度信息为目的将点云数据规则化为灰度(体素内激光点的平均强度的离散化表示)体素结构,然后基于各体素间的空间连通性和灰度相似性准则,将灰度体素结构分割并标记为若干个3D连通区域,最后依据地面与其它目标的高差特性提取与其对应的3D连通区域。算法优势在于:基于体素结构设计,为3D滤波算法;综合利用了地面目标的几何及辐射特征,对比传统滤波算法可应用于更复杂的场景;滤波结果为3D地面体素形式,可直接用于创建地面3D模型。实验采用国际摄影测量与遥感协会(International Society for Photogrammetry and Remote Sensing, ISPRS)提供的不同密度的机载LiDAR基准测试数据测试了邻域尺度参数的敏感性及提出的算法的有效性,并和其他经典滤波算法做对比。定量评价的结果表明,51邻域为最佳空间邻域尺度;点云密度为0.67点/m2的数据集1的滤波平均完整率、正确率及质量分别为0.9611、0.9248及0.8934;点云密度为4点/m2的数据集2的滤波平均完整率、正确率及质量分别为0.8490、0.8531及0.7404;对比其全经典滤波算法本文算法在高密度点云数据滤波时表现更佳。

关键词: 激光雷达, 滤波, 体素结构, 3D连通区域, 几何特征, 辐射特征, 分割, 高斯混合模型

Abstract:

Existing filtering algorithms based on airborne LiDAR data have many disadvantages, e.g., they fail to make full use of all the information of the LiDAR data, and their data structures are complex or suffer from information loss. In this paper, we proposed an airborne LiDAR 3D filtering algorithm based on the grayscale voxel structure segmentation model. The proposed algorithm ?rst regularizes a given LiDAR point cloud into a grayscale voxel structure to comprehensively utilize the elevation and intensity information of the LiDAR data, in which the voxel's grayscale denotes the discretized mean intensity of LiDAR points within the voxel. Then, the constructed grayscale voxel structure is segmented into multiple 3D connected regions depending on the spatial connectivity and grayscale similarity among voxels. Finally, the 3D connected regions corresponding to ground objects are detected based on elevation differences. The proposed algorithm has the following advantages. (1) It is designed based on the grayscale voxel structure and is a 3D filtering algorithm. (2) The average completeness, correctness and quality of the proposed filtering algorithm for dataset 1 with 0.67 points per square meter were 0.9611, 0.9248, and 0.8934. The average completeness, correctness and quality of the proposed filtering algorithm for dataset 2 with 4 points per square meter were 0.8490, 0.8531, and 0.7404. (3) Its filtering result is in the form of the 3D ground voxel, which can be directly used for creating a 3D model of ground. The sensitivity of "spatial adjacency size" parameter in the model and the validity of the proposed algorithm is analyzed by using two airborne LiDAR benchmark test datasets of different densities provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), and the accuracy of the proposed filtering algorithm is compared with that of the other classical filtering algorithms. Our experimental results of quantitative evaluation show that: (1) The 56-adjacency was the optimal adjacent size. (2) The average completeness, correctness and quality of the proposed filtering algorithm for dataset 1/2 with 0.67/4 points per square meter were 0.9611/0.8490, 0.9248/0.8531, and 0.8934/0.7404, respectively. (3) Compared with other classical filtering algorithms, the proposed algorithm performed better in high-density point cloud data filtering.

Key words: LiDAR, filtering, voxel structure, 3D connected region, geometric characteristic, radiometric characteristic, segmentation, gaussian mixture model