基于法向量距离的路面坑槽提取方法

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,...

Extraction of Pavement Potholes Based on Normal Vector Distance

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.