Automatic extraction of highway light poles and towers from mobile LiDAR data
Automatic extraction of highway light poles and towers from mobile LiDAR data
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DOI：
10.1016/j.optlastec.2015.09.017
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发表时间：
2016-03
影响因子：
5
通讯作者：
Wai Yeung Yan;S. Morsy;A. Shaker;M. Tulloch
中科院分区：
文献类型：
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作者：
Wai Yeung Yan;S. Morsy;A. Shaker;M. Tulloch
移动激光雷达最近被证明是一种用于杆状物体检测和分类的可行技术。尽管现有研究报告了理想的准确度（约 80%），但大多数研究都是在地面相对平坦的街道水平上提出的，很少涉及如何从地面或路缘表面提取整个杆结构。因此，本文试图通过提出一种从移动激光雷达数据点云自动提取灯杆和塔的工作流程来填补研究空白，特别关注市政高速公路。数据处理工作流程包括（1）自动地面过滤机制，用于分离地上和地面特征；（2）无监督聚类算法，用于对地上数据点云进行聚类；（3）一套决策规则，用于识别和分类潜在的灯杆和灯塔；（4）最小二乘圆拟合算法，用于拟合圆杆结构，从而去除地面点。该工作流程使用一组移动 LiDAR 数据进行了测试，这些数据是为加拿大安大略省多伦多市 401 号高速公路的一段路段收集的。结果表明，该方法对研究区沿线五类灯杆塔的检测率达到91%以上。
Mobile LiDAR has been recently demonstrated as a viable technique for pole-like object detection and classification. Despite that a desirable accuracy (around 80%) has been reported in the existing studies, majority of them were presented in the street level with relatively flat ground and very few of them addressed how to extract the entire pole structure from the ground or curb surface. Therefore, this paper attempts to fill the research gap by presenting a workflow for automatic extraction of light poles and towers from mobile LiDAR data point cloud, with a particular focus on municipal highway. The data processing workflow includes (1) an automatic ground filtering mechanism to separate aboveground and ground features, (2) an unsupervised clustering algorithm to cluster the aboveground data point cloud, (3) a set of decision rules to identify and classify potential light poles and towers, and (4) a least-squares circle fitting algorithm to fit the circular pole structure so as to remove the ground points. The workflow was tested with a set of mobile LiDAR data collected for a section of highway 401 located in Toronto, Ontario, Canada. The results showed that the proposed method can achieve an over 91% of detection rate for five types of light poles and towers along the study area.