摘要
我国铁路隧道监控采用全站仪、水准仪等设备的传统量测方法,存在自动化、智能化水平低,以及现有基于三维激光扫描技术的隧道断面自动监控量测方法存在前后2次监测测点不对应等问题。提出将传统测量与三维激光扫描技术相结合的隧道监控量测方法(简称结合量测法),首先扫描平面监测标靶,通过生成隧道面缓冲区并切割隧道面点云获得监测标靶点云,然后通过k-means聚类方法自动识别监测标靶中心坐标,从而完成标靶自识别的自动化隧道监控量测。进一步比较传统标靶量测方法、点云处理软件识别标靶和结合量测法识别标靶的坐标误差及多站相对距离测量差,结果表明结合量测法标靶识别方法坐标测量内符合精度小于0.5 mm,以高精度全站仪测值作为真值时的外符合精度小于1.0 mm,总体符合铁路隧道监控量测1.0 mm的精度要求。
The traditional railway tunnel monitoring measurement method with devices such as total station and level gauge cannot satisfy the increasing demand for higher level of automation and intelligence,and the current automatic monitoring measurement method of tunnel section based on 3D laser scanning technology leads to problems such as mismatch of monitoring points during two successive rounds of monitoring.This paper proposes a tunnel monitoring measurement method integrating traditional measurement and 3D laser scanning technology(referred to integrated measurement method for short).First,the plane monitoring target is scanned,the monitoring target point cloud is obtained by setting the tunnel surface buffer zone and segmenting the tunnel surface point cloud,and then the center coordinates of the monitoring target are automatically identified with k-means clustering method.Thus,the automatic tunnel monitoring measurement of automatic target identification is completed.Through further comparison of coordinate error and multi-station relative distance measurement difference between the traditional target measurement method,the point cloud processing software target identification and the combined measurement method target identification,the results show that internal coincidence accuracy of coordinate measurement from the combined measurement method target identification method is less than 0.5 mm,and the external coincidence accuracy is less than 1.0 mm when the measured value of high-precision total station is taken as the true value,which generally meets the accuracy requirements of 1.0 mm for railway tunnel monitoring measurement.
作者
郑荣政
吴勇生
苏哿
张浩
杨承昆
ZHENG Rongzheng;WU Yongsheng;SU Ge;ZHANG Hao;YANG Chengkun(Civil Engineering Design Research Institute,China Railway Design Corporation,Tianjin 300308,China;Hunan Lianzhi Technology Co Ltd,Changsha Hunan 410200,China)
出处
《铁路技术创新》
2022年第3期36-41,共6页
Railway Technical Innovation
基金
中国铁路设计集团有限公司科技研究开发计划项目(2021B240629)
飞泰交通科技有限公司科技开发课题(2021FTKY05)。
关键词
铁路隧道
监控量测
三维激光扫描
点云分割
K-MEANS聚类
标靶自动识别
railway tunnel
monitoring measurement
3D laser scanning
point cloud segmentation
k-means clustering
automatic target identification
