基于激光三维点云分割地铁隧道壁表面物体  

Segmentation of Subway Tunnel Wall Surface Objects Based on Laser 3D Point Cloud

作　　者：曹桂萍 刘行思 刘念 杨克成[1] 夏珉[1] Guiping Cao;Xingsi Liu;Nian Liu;Kecheng Yang;Min Xia(School of Optical and Electronic Information,Huazhong University of Science and Technology,Wuhan,Hubei 430074,China)

机构地区：[1]华中科技大学光学与电子信息学院,湖北武汉430074

出　　处：《光学学报》2020年第21期32-40,共9页Acta Optica Sinica

摘　　要：从地铁隧道三维点云数据中分割出物体的点云是自动化检测地铁隧道病害及重建地铁隧道三维模型的关键步骤。由于某自动化检测系统的结构特点,使用其采集的三维点云数据计算点云法线向量和曲率时准确度不高,导致一些常用的三维点云分割算法,比如一种改进的区域生长分割法不适用于该检测系统采集的点云数据。为了分割某自动化检测系统采集的三维点云数据,设计并实现了一种基于密度聚类的分割算法。这种算法避免使用不准确的法线向量和曲率,克服了某自动化检测系统的缺点,并用实际三维点云数据对比了区域生长分割法和基于密度聚类分割算法的分割结果。Segmenting the point cloud from the 3D point cloud data of a subway tunnel is a key step to automatically detect the damage of the subway tunnel and reconstruct a 3D model of the tunnel. The collected 3D point cloud data are inaccurate for calculating the normal vector and curvature of the point cloud because of the structural characteristics of an automated detection system. This renders some common 3D point cloud segmentation algorithms, such as an improved region growing segmentation method, unsuitable for the point cloud data collected by the detection system. To segment the 3D point cloud data collected by an automated detection system, an algorithm based on density clustering was designed and implemented. This algorithm avoids the use of inaccurate normal vector and curvature, overcoming the limitations of an automatic detection system. Finally, we compared the segmentation results of the region growing segmentation method with those of the designed segmentation algorithm based on density clustering using the actual 3D point cloud data.

关 键 词：图像处理 三维点云 点云分割 基于密度聚类 隧道检测 

分 类 号：TN249[电子电信—物理电子学]