为快速、准确获取施工隧道初期支护整体及局部变形信息,基于计算机视觉算法获取的隧道初期支护图像稀疏点云和密集点云数据,结合基于Hausdorff 距离的整体模型和基于最小二乘拟合平面的局部模型各自的特点,提出将两者相结合的图像点云空间测距算法。该方法可为每个点保留整体模型和局部模型分别计算的距离值中的较小值,解决前者对点云密度要求高以及后者局部拟合平面存在较大偏差问题,实现多期隧道图像点云直接比较分析,简化计算步骤和后处理过程,提高隧道初期支护变形监测速率和精度。通过对云南香丽高速公路白岩子隧道进口左线ZK61+990~ +994 段初期支护整体变形的监测分析,结果表明: 该技术能直观、可视化反映隧道初期支护整体变形情况,计算结果准确、可靠。

In order to aquire the global and local deformation of primary support rapidly and accuratly, an image point cloud space distance measurement algorithm is put forward based on image sparse point cloud and dense point cloud of tunnel primary support obtained by computer vision algorithm and the characteristics of the global model based on Hausdorff distance and local mode based on least square fitting plane. By using the algorithm mentioned-above, the smaller distance calculated by the global model and local model can be reserved for each point, which helps to solve the problems that global model has high requirements on point cloud density and local model has large deviation in local fitting plane; further more, the direct comparison among multi-stage tunnel image point clouds can be realized, and the calculation steps and post-processing process can be simplified, which improves the monitoring speed and accuracy. The algorithm is applied to the global deformation monitoring of primary support of section ZK61+990~ +994 of left line of Baiyanzi Tunnel on Shangri-la-Lijiang Expressway in Yunnan, and the monitoring results show that the algorithm can directly and visually reflect the overall deformation of the tunnel, and the calculation results are accurate and reliable.