A method based on an adaptive radius cylinder model for detecting pole-like objects in mobile laser scanning data
A method based on an adaptive radius cylinder model for detecting pole-like objects in mobile laser scanning data
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DOI：
10.1080/2150704x.2015.1126377
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发表时间：
2016-03
影响因子：
2.3
通讯作者：
Lin Li;You Li;Dalin Li
中科院分区：
文献类型：
--
作者：
Lin Li;You Li;Dalin Li
本文提出了一种基于自适应半径圆柱模型（MBARCM）的方法，用于检测移动的激光扫描数据中的柱状目标。首先，对散乱的点云进行体素化，对原始数据进行压缩和组织。其次，自下而上的跟踪算法来检测潜在的垂直对象的位置，并合并相邻段。最后，根据竖向段的选取层数，建立自适应半径圆柱模型，并将其应用于合并竖向段进行隔震分析。所提出的方法的性能进行了验证，在三个测试站点的两个数据集。使用所提出的方法，完整性值范围从94.6%到97.7%的三个网站测试，而正确性值从79%到100%。
ABSTRACT This letter presents a method based on an adaptive radius cylinder model (MBARCM) for detecting pole-like objects in mobile laser scanning data. First, the unorganized point clouds are voxelized to compress and organize the original data. Second, the bottom-up tracing algorithm is implemented to detect potential vertical-object locations, and the neighbour segments are merged. Finally, the adaptive radius cylinder model is built based on the selected layers of the vertical segment, and the model is applied to the merged vertical segments to perform isolation analysis. The performance of the proposed method is validated on three test sites for two datasets. Using the proposed method, the completeness values ranged from 94.6% to 97.7% for the three sites tested, while the correctness values were from 79% to 100%.