基于法向量距离的路面坑槽提取方法  

Extraction of Pavement Potholes Based on Normal Vector Distance

作　　者：陈鑫龙 马荣贵[1] 梁红涛 廖飞钦 CHEN Xin-Long;MA Rong-Gui;LIANG Hong-Tao;LIAO Fei-Qin(School of Information Engineering,Chang’an University,Xi’an 710064,China;Baoji Branch,Shaanxi Transportation Holding Group Co.Ltd.,Baoji 721399,China)

机构地区：[1]长安大学信息工程学院,西安710064 [2]陕西交通控股集团有限公司宝鸡分公司,宝鸡721399

出　　处：《计算机系统应用》2022年第5期222-229,共8页Computer Systems & Applications

基　　金：陕西省交通运输厅交通科研项目(20-24K,20-25X)。

摘　　要：以路面高程激光点云为研究对象,提出一种基于法向量距离的路面坑槽提取方法.首先对路面高程点云数据进行数据清洗;其次采用自适应最优邻域的PCA方法估算路面点云数据的法向量,通过计算路面点云中采样点到其局部二次曲面的切平面的法向距离作为法向量距离;以法向量距离描述采样点的三维空间特征,并通过阈值分割自动提取路面坑槽点云集合,通过Mean-Shift聚类算法分割路面坑槽点云集合得到多个坑槽点集;最后针对每个坑槽点集,采用Alpha Shape算法提取坑槽边界点,对坑槽边界点进行三次样条插值拟合得到坑槽轮廓,据此计算坑槽尺寸(长度、宽度、深度)、面积信息.以规则坑槽模型点云数据与真实路面点云数据进行实验,本文方法提取坑槽的深度的相对误差的均值分别为2.7%,4.7%,提取坑槽面积的相对误差的均值分别为6.8%,4.3%.实验结果表明本文方法可以精确提取路面坑槽边界点及其尺寸信息,且对于不规则形状坑槽的识别及提取具有较强的抗干扰性.Taking the laser point cloud of pavement elevation as the research object,this study proposes a method for extracting pavement potholes based on normal vector distance.Firstly,the cloud data of pavement elevation points are cleaned.Secondly,the PCA method in the adaptive optimal neighborhood is used to estimate the normal vector of the pavement point cloud data.The normal distance from the sampling point in the pavement point cloud to the tangent plane of its local quadric surface is calculated as the normal vector distance to describe the three-dimensional spatial features of the sampling points.Next,threshold segmentation is employed to automatically extract the pothole point cloud set,which is then segmented by the Mean-Shift clustering algorithm to obtain multiple pothole point sets.Finally,for each pothole point set,the Alpha Shape algorithm is used to extract pothole boundary points that are fitted by cubic spline interpolation three times to obtain the pothole contour.On this basis,the pothole size(length,width,and depth)and area are calculated.Experiments are carried out on regular pothole model point cloud data and real pavement point cloud data.The calculation shows that the average relative errors of pothole depth extracted by this method are 2.7%and 4.7%,respectively,and the average relative errors of pothole area extracted by this method are 6.8%and 4.3%,respectively.The experimental results show that the proposed method can accurately extract the boundary points and size information of pavement potholes and has a strong anti-interference ability for the recognition and extraction of irregular-shaped potholes.

关 键 词：特征提取 目标检测 坑槽 数据清洗 MEAN-SHIFT 自适应最优邻域 法向量距离 Alpha Shape算法 

分 类 号：U463.6[机械工程—车辆工程] TP391.41[交通运输工程—载运工具运用工程]