基于中心轴正交旋转的点云数据隧道断面提取方法

Tunnel Point Cloud Section Extraction Method Based on Orthogonal Rotation of the Central Axis

隧道断面的精确提取是隧道形变分析中的一个关键环节,然而,隧道内部复杂的形状和环境使得点云处理算法变得复杂,传统的断面提取与变形分析算法已经不适用于点云数据,因此亟需更高效和更鲁棒的算法。针对这一问题,本文提出了一种基于点云数据的隧道断面连续提取方法。首先,引入结合随机样本一致性(Random Sample Consensus,RANSAC)圆柱拟合与半径滤波的方法,以有效滤除离群点及附着在隧道壁上的噪声点;接着,通过双向投影获取隧道中轴线,并根据“与隧道中轴线正交旋转的直线相交于隧道壁”建立数学模型,从而实现对隧道点云断面的连续提取;最后,以断面点的拟合圆心坐标和设计半径为参数计算隧道的形变量,并通过可视化渲染直观展示隧道整体变形情况。为证明本文方法的有效性,实验选取了成都市某地铁线路三段地铁隧道的激光点云数据,结果表明,该方法滤除噪声时Ⅰ类误差、Ⅱ类误差和总误差均值分别为1.48%、1.03%和1.21%,Kappa系数达到97.45%。相比传统的最小二乘法、密度聚类法以及法向偏差法,本方法的3类误差之和分别降低了9.34%、10.61%和4.41%,Kappa系数则提高了5.36%、6.38%和3.65%。这充分展现出本方法更优越的鲁棒性和准确性;此外,该方法所提取出的隧道断面拟合半径与设计半径的偏差均值低至1.36 mm。相比目前的1.60 mm方法及6.00 mm方法,本文方法的偏差均值分别降低了2.5 mm和2.7 mm。而求得隧道形变量范围在0~18 mm之间,同时通过点云渲染方式直观展示了隧道整体变形情况。本研究的方法为隧道安全监测工作提供了可靠依据和基础支持。

The accurate extraction of tunnel sections is a pivotal step in tunnel deformation analysis.However,due to inadequate illumination,the reflection and occlusion caused by dust and structural elements generate noise and erroneous points in the point cloud data,complicating data processing.Additionally,the intricate geometry of tunnel features,such as curved surfaces,corners,and cracks,renders traditional section extraction algorithms ineffective for point cloud data.Consequently,there is an urgent need for more efficient and robust algorithms.To address this issue,this paper proposes a method for continuous tunnel section extraction based on laser point cloud data.First,a combination filtering method is introduced,integrating Random Sample Consensus(RANSAC)cylindrical fitting and radius filtering to effectively remove scattered outliers and noise points adhering to the tunnel walls with sparse density.Next,the tunnel central axis is obtained via bidirectional projection,and a mathematical function model is established in line with the principle that‘a straight line intersecting the tunnel central axis intersects the tunnel wall’,enabling the continuous extraction of tunnel section point clouds.Finally,the fitting center coordinates of the section points and the tunnel design radius are used as parameters to calculate the radial deviation of the tunnel points,representing the shape variables.The tunnel point cloud is visually rendered using these shape variables to display the overall deformation of the tunnel.In this paper,laser point cloud data from three sections of a subway tunnel in Chengdu are used as the experimental data.The results show that the mean values of Class I error,Class II error,and total error are 1.48%,1.03%,and 1.21%,respectively,with the Kappa coefficient reaching 97.45%when using this method for noise filtering.Compared to traditional methods such as least squares,density clustering,and normal deviation algorithms,this method reduces cumulative errors by 9.34%,10.61%,and 4.41%,respectively,while increasing the Kappa coefficient by 5.36%,6.38%,and 3.65%.This demonstrates the enhanced robustness and accuracy of the proposed method.Moreover,the mean deviation between the tunnel section fitting radius obtained through this method and the design radius is merely 1.36 mm,compared to deviations of 1.60 mm and 6.00 mm with existing methods,achieving reductions of 2.5 mm and 2.7 mm,respectively.The range of the tunnel shape variable is between 0 and 18 mm,and the overall deformation of the tunnel is visually displayed through point cloud rendering.The method provides a reliable foundation and essential support for tunnel safety monitoring.