顾及邻域局部特征的车载点云城市道路提取

Urban road extraction of vehicle point cloud considering localfeatures of neighborhood

本文针对区域生长算法导致城市道路提取过程中点云的过分割问题,结合点云空间邻域特征信息,提出了一种用于提取地面点云的改进区域生长算法。首先进行数据预处理,去除远离城市环境的离群点;其次建立二维空间虚拟格网,合理利用点云的空间局部性、减少运算规模;然后采用平均曲率约束和拟合平面夹角约束聚类提取出城市道路点云;最后利用两段城市道路点云进行试验,与现有的区域生长算法进行对比分析。试验结果表明,本文方法能够很好地兼顾提取完整度与准确度,在复杂城市道路点云提取和城市道路建模中具有实用性。

Aiming at the problem of over-segmentation of point cloud in the process of urban road extraction caused by region growing algorithm,an improved region growing algorithm is proposed to extract ground point cloud by combining the spatial neighborhood feature information of point cloud.Firstly,data preprocessing is carried out to remove outliers far from the urban environment.Secondly,a two-dimensional spatial virtual grid is established to make rational use of the spatial locality of point cloud and reduce the scale of operation.Then,the urban road point cloud is clustered by the growth range of the mean curvature constraint region growth and the angle constraint of the fitting plane.Finally,two urban road point clouds are used for experiments,and compared with the existing region growing algorithm.The experimental results show that the proposed method can well balance the integrity and accuracy of extraction,and is practical in complex urban road point cloud extraction and urban road modeling.