基于3D 移动测量系统点云数据的钢轨信息自动提取方法
Automatic Rail Information Extraction Method Based on Point Cloud Data of 3D Mobile Measurement System
- 机车电传动 2021年第2期 页码：114-119
- 作者机构：
1.株洲时代电子技术有限公司，湖南 株洲 412007
- 作者简介：
张东方（1980—），男，高级工程师，研究方向为电气工程及自动化和铁路线路基础设施智能检测技术；E-mail：zhangdf@csrzic.com
- 基金信息：“十三五”国家重点研发计划项目(2016YFB1200401)
DOI：10.13890/j.issn.1000-128x.2021.02.018
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张东方, 谷利元, 季育文, 等. 基于3D 移动测量系统点云数据的钢轨信息自动提取方法[J]. 机车电传动, 2021,(2):114-119.
Dongfang ZHANG, Liyuan GU, Yuwen JI, et al. Automatic Rail Information Extraction Method Based on Point Cloud Data of 3D Mobile Measurement System[J]. Electric Drive for Locomotives, 2021,(2):114-119.
张东方, 谷利元, 季育文, 等. 基于3D 移动测量系统点云数据的钢轨信息自动提取方法[J]. 机车电传动, 2021,(2):114-119. DOI： 10.13890/j.issn.1000-128x.2021.02.018.
Dongfang ZHANG, Liyuan GU, Yuwen JI, et al. Automatic Rail Information Extraction Method Based on Point Cloud Data of 3D Mobile Measurement System[J]. Electric Drive for Locomotives, 2021,(2):114-119. DOI： 10.13890/j.issn.1000-128x.2021.02.018.
摘要
为实现铁路基础设施智能化管理运维，利用3D移动测量系统高效获取高精度点云数据，并对点云数据进行智能化处理。钢轨作为铁路基础设施中最基本的单元，是几何参数计算的基础，对其信息进行自动提取具有重要意义。因此，提出了一种基于3D移动测量系统点云数据的钢轨信息自动提取方法。首先利用点云数据中的角度信息快速实现道床区域的分割，有效减小计算量；然后利用精细栅格划分和动态阈值实现地面点与非地面点的分离；最后利用DBSCAN聚类算法与RANSAC算法完成钢轨点云数据的最终提取。为验证该算法的有效性，以国铁场景钢轨点云数据和隧道场景钢轨点云数据为试验对象，验证结果显示国铁场景和隧道场景的钢轨点云提取准确度分别为96.32%和97.54%，完整度分别为92.14%和94.87%，准确度和完整度均高于90%。试验结果表明：该方法具有操作简单，提取结果准确的优点。
Abstract
In order to realize the intelligent management and maintenance of railway infrastructure, it's necessary to use 3D mobile measurement system to ef fi ciently obtain high-precision point cloud data and intelligently process the point cloud data. As the basic unit of railway infrastructure, rail track is the basis of geometric parameters calculation, so it is of great signi fi cance to automatically segment the rail track. Based on this, an automatic rail information extraction method was proposed based on 3D mobile measurement system point cloud data in this paper. Firstly, the angle information in the point cloud data was used to segment the track bed area, which effectively reduces the amount of calculation. Then, the ground points and non-ground points were separated by using fi ne grid division and dynamic threshold. Finally, DBSCAN clustering algorithm and RANSAC algorithm were used to complete the fi nal extraction of rail point cloud data. In order to verify the effectiveness of the proposed algorithm, rail point cloud data of national railway scene and rail point cloud data of tunnel scene were selected for processing. The rail point cloud extraction accuracy of the railway scene and the tunnel scene were 96.32% and 97.54%, respectively, and the completeness were 92.14% and 94.87%respectively. The accuracy and completeness were both higher than 90%. The experimental results showed that the algorithm has the advantages of simple operation and accurate extraction results.
关键词
移动测量系统点云数据钢轨栅格划分动态阈值轨道交通
Keywords
mobile measurement systempoint cloud datarail trackgrid divisiondynamic thresholdrail transit
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