Automatic extraction of spatially relevant 3D LiDAR railway support facility

doi:10.3785/j.issn.1008-973X.2022.10.018

Journal of ZheJiang University (Engineering Science)

2022, Vol. 56

Issue (10): 2066-2076 DOI: 10.3785/j.issn.1008-973X.2022.10.018

Automatic extraction of spatially relevant 3D LiDAR railway support facility

Yuan-wei YANG1,2,3(

),Yue ZHANG1,Xian-jun GAO1,Bing-jie MA1,Shen-ao GUO1,Lei XU4

1. School of Geosciences, Yangtze University, Wuhan 430100, China
2. Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China
3. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, Beijing 100045, China
4. China Railway Design Corporation Limited Company, Tianjin 300251, China

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Abstract

An automatic extraction algorithm of 3D LiDAR support facilities considering the spatial relationship was proposed in order to improve the extraction reliability and accuracy of multi-type support facilities. The key trajectory points were obtained by thinning the GNSS trajectory points. Then the original data was layered and divided into blocks to obtain the original point cloud pillar and support device data area. The center point of the pillar was obtained through the neighborhood search, and a spatial index was constructed based on the spatial relationship between the trajectory point and the search layer of the support device. The index was used as the driving force to realize the initial extraction of the support device and obtain the support device containing the contact line. The column search and parametric projection filtering were introduced to filter out the contact line point cloud for optimization of extraction results. The test results show that the algorithm has more than 93% MIoU and 94% Dice coefficient for extracting support facilities, which can consider multiple types of support facilities and has strong robustness and application value.

Key words： detection of catenary spatial index 3D LiDAR support facility extraction point cloud neighborhood search

Received: 13 January 2022 Published: 25 October 2022

CLC:

U 225

Fund: 城市轨道交通数字化建设与测评技术国家工程实验室开放课题基金资助项目(2021ZH02)；湖南科技大学测绘遥感信息工程湖南省重点实验室开放基金资助项目(E22133)；城市空间信息工程北京市重点实验室经费资助项目(20210205)；海南省地球观测重点实验室开放基金资助项目(2020LDE001)；长江大学大学生创新创业训练计划资助项目(Dk2021013)

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	Yuan-wei YANG
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	Lei XU

Cite this article:

Yuan-wei YANG,Yue ZHANG,Xian-jun GAO,Bing-jie MA,Shen-ao GUO,Lei XU. Automatic extraction of spatially relevant 3D LiDAR railway support facility. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 2066-2076.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.10.018 OR https://www.zjujournals.com/eng/Y2022/V56/I10/2066

顾及空间关系的3D LiDAR铁路支持装置自动提取

为了提高不同类型支持装置在不同场景中的提取可靠性及精度，提出顾及空间关系的3D LiDAR支持装置自动提取算法. 通过对GNSS轨迹点抽稀处理获取关键轨迹点，对原始数据进行分层、分块处理，获取原始点云支柱，支持装置数据区域. 通过邻域搜索获取支柱中心点，依据其与轨迹点和支持装置搜索层的空间关系构建空间索引. 以该索引为驱动实现支持装置的初提取，获取含有接触线的支持装置，引入柱状搜索、参数化投影滤波的方式滤除接触线点云，实现提取结果的优化. 经过测试可知，该算法对提取支持装置的MIoU均超过93%，Dice系数均超过94%，可以兼顾多类型支持装置，具有较强的鲁棒性和应用价值.

关键词： 接触网检测, 空间索引, 三维激光点云, 支持装置提取, 点云邻域搜索

Fig.1 Point cloud data acquisition equipment

Tab.1 Specific parameters of Z+F Profiler 9012 measurement system

Fig.2 Frame diagram of supporting facilities extraction

Fig.3 Schematic diagram of trace point thinning processing

Fig.4 Schematic diagram of affine transformation process

Fig.5 Schematic diagram of hierarchical and block processing

Fig.6 Schematic diagram of pillar center point extraction

Fig.7 Diagrams of spherical search and column search

Fig.8 Schematic diagram of rough cut

Fig.9 Schematic diagram of contact wire filtering

Tab.2 Table of dilution experimental results

Fig.10 Classification diagram of target object in OCS profile

Fig.11 Extraction effect of support facility

Fig.12 Comparison chart of test accuracy and threshold value of linear device

Fig.13 Comparison of extraction results of different types of support facilities

Fig.14 Accuracy comparison chart of proposed algorithm and comparison algorithm

Tab.3 Time analysis table of multitype support facilities

Fig.15 Display of extraction effect of pillar distribution in different situations

Tab.4 Comparison of test results under different conditions of pillar distribution

Fig.16 Ten kilometers of real railway scene extraction renderings (partial)


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