利用点云数据的煤矿隧道变形检测方法

Jitong Kang, Mei Li, Shanjun Mao, Yingbo Fan, Zheng Wu, Ben Li
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摘要

近年来,地下巷道变形检测技术在煤矿安全管理中发挥着至关重要的作用。目前,煤矿巷道变形检测主要采用交叉法、顶板剥离层监测仪等传统方法。随着摄影测量方法的发展,三维激光扫描仪逐渐成为煤矿巷道变形检测的主要方法。然而,由于煤矿巷道的密闭空间风险高、分布距离远,固定式三维激光扫描技术需要耗费大量人力和时间,存在一定的操作风险。目前,移动激光扫描已成为煤矿巷道变形检测的常用方法。本文提出了一种基于手持式三维激光扫描仪的煤矿井下巷道点云变形检测方法。该方法利用 SLAM 激光雷达获取整个隧道的完整点云信息,同时将三维点云投影到不同的平面上,从而获得隧道中心线的坐标。利用计算出的隧道中心线,将不同时间采集的三维点云数据匹配到同一坐标系,然后分别从全局和断面角度分析隧道变形参数。通过现场采集隧道数据,本文验证了该算法的可行性,并与其他中心线拟合和点云注册算法进行了比较,证明了该算法具有更高的精度,满足了实际需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data
In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.
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