基于中轴线的隧道点云去噪算法

Tunnel Point Cloud Denoising Algorithm Based on Centerline

摘要:

隧道作为一个狭长的封闭空间，其点云内部噪声影响点云分析精度，有效去除隧道点云内部的噪声是基于点云隧道形变分析的关键.提出一种基于中轴线的隧道点云去噪算法.通过对点云双向投影获取隧道在水平和垂直方向的姿态变化，根据高阶多项式拟合两条平面曲线并插值中轴线控制点，通过定义空间线段的夹角加密控制点以表达中轴线.通过计算各控制点处的切平面实现对隧道点云的分割，计算各分块内点到中轴线的距离，并根据给定的距离阈值实现隧道内部点云噪声的过滤.通过两组实验分析证实该方法的可行性与精确性.第一组通过模拟隧道点云数据并采用该方法拟合中轴线，比较分析其与已知中轴线的精度.第二组通过分析处理实际的隧道点云数据，实现隧道点云内部噪声的去除.

Abstract:

The noise in the point cloud data of a tunnel affects the accuracy of analysis. Effectively removing the noise data has become the key factor in point cloud based tunnel deformation analysis. A tunnel point cloud denoising algorithm based on centerline was proposed in this paper. First, the gesture of the point cloud was obtained by projecting it onto the horizontal plane and vertical plane respectively, and fitting two curves by high order polynomial equations from the planar data out of which the control points of the centerline were then interpolated. The control points were are used to express the centerline were densified according to the intersection angle between the spatial lines. Meanwhile, the tangent planes at each control point were computed to segment the tunnel point cloud. In addition, the distances from each point to the centerline in each block were computed to compare with the given distance threshold to accomplish data denoising. The feasibility and accuracy of the proposed method were demonstrated by two experiments. The first test is to fit the centerline by simulating the tunnel point cloud while the fitting accuracy of the centerline was analyzed by comparing it with the given one. The second test was to analyze the practical tunnel point cloud. Data denoising was finally achieved by implementing the proposed method.