为了解决隧道形变检测准确性低、分析不全面的问题，对隧道三维点云轴线提取、隧道影像生成及分割、隧道点云数据分割和隧道断面形变检测等方面进行研究。对于三维激光扫描仪获取到的隧道原始点云数据，首先基于设计轴线，对RANSAC算法进行改进，来拟合等间距隧道断面圆心，并采用基于最小二乘优化的3次B样条曲线拟合方法，获取隧道实测轴线； 其次，通过从点云数据到影像的数据转换方法，生成隧道反射率影像，并进一步利用点云数据几何特征生成几何权重图，应用边界检测提取隧道影像中的边界线；
然后，将边界线像素映射至隧道三维点云数据空间，实现隧道点云数据的分割；最后，设计一种精确的隧道断面形变分析方法，分析断面形变。试验表明： 本算法可精确分割隧道点云数据，提高隧道断面形变检测的准确度和可靠性。
To improve the deformation detection and analysis accuracy of a tunnel, studies are conducted on the axis extraction of a 3D tunnel point cloud, generation and segmentation of tunnel intensity image, pointcloud segmentation of tunnel, and deformation detection of tunnel crosssection. Bassed on the original pointcloud data of the tunnel obtained using a 3D laser scanner, an improved RANSAC algorithm is proposed to fit the center of the equaldistance tunnel crosssection based on the designed axis, and a fitting method of cubic Bspline curve is applied to obtain the spatial axis of the tunnel based on the least square method. Furthermore, the tunnel intensity image is generated using algorithm of data transformation, and the boundary detection and geometric weights calculation algorithms are designed to extract all boundary lines in the image. By mapping the boundary line pixels to the threedimensional space, the segmentation of the tunnel point cloud is achieved. Finally, accurate detection metrics for tunnel crosssection deformation is proposed. The results show that the algorithm obtained can segment the tunnel point cloud and improve the accuracy and feasibility of tunnel crosssection deformation detection.
