基于地铁隧道点云数据的组合滤波算法

Combined Filtering Algorithm Based on Point Cloud Data of Metro Tunnel

目的提出一种点云数据组合的滤波方法,对地铁隧道的点云数据进行有效的精简滤波,提高地铁隧道结构变形分析的准确性.方法首先,对三维点云数据采用基于统计特征的滤波方法进行初步滤波,去除远离点云数据主体的离散点;其次,估算点云数据模型各数据点的高斯曲率,将点云数据分为突变区域和平滑区域;最后,采用双边滤波算法对突变区域点云数据进行滤波,采用改进的均值滤波算法对平滑区域点云数据进行滤波处理.结果通过对沈阳地铁隧道点云数据进行滤波实验及拟合圆半径分析,笔者所提组合滤波算法可以在保留隧道壁和轨道等结构的情况下,去除离散点和隧道壁上的无关非点等噪声;该算法对点云数据进行了有效精简,拟合圆的半径与设计半径差值更小,结果精度更高.结论笔者所提出的滤波算法可去除地铁三维点云数据的噪声点,并完整保留了隧道结构的几何细节特征,提高了变形分析的精度.

This paper presents a combined filtering method for point cloud data,effective reduction filtering for point cloud data of Metro Tunnel,improve the accuracy of structural deformation analysis of subway tunnel.Firstly,the three-dimensional point cloud data is preliminarily filtered using a filtering method based on statistical features to remove discrete points away from the main body of point cloud data.Secondly,Estimate the Gaussian curvature of each data point in the point cloud data model,and divide the point cloud data into mutation region and smooth region.Finally,use the bilateral filtering algorithm to filter the point cloud data in the mutation region,and use the improved mean filtering algorithm to filter the point cloud data in the smooth region.According to filtering experiment and fitting circle radius analysis on point cloud data of Shenyang Metro Tunnel,the combined filtering algorithm proposed in this paper can remove the independent non-point noise of discrete points and tunnel walls while retaining the structure of tunnel walls and tracks.The point cloud data is effectively simplified,the radius of the fitting circle is closer to the design radius,and the accuracy is higher.The filtering algorithm proposed by the author can remove the noise points of the three-dimensional point cloud data of the Metro Tunnel,and completely retain the geometric details of the tunnel structure,which can improve the accuracy of the deformation analysis.