车载LiDAR点云路灯提取方法

Point Cloud Information Extraction for Streetlights with Vehicle-borne LiDAR

道路场景中路灯数量大、类型多,大场景中路灯详细信息获取是一项繁重的工作。本文提出先验样本集辅助的、基于骨架线缓冲区判别的路灯点云提取及种类识别算法:先根据路灯在车载LiDAR点云中的表达特征,构建路灯模型,并构建路灯先验样本集;再依据数学形态学的理论和方法,提取车载LiDAR点云场景中的杆状地物,在路灯模型及语义规则约束下,得到候选路灯;然后根据候选路灯的参数信息,及已获取路灯的统计信息,从样本集中筛选候选样本;最后基于最小二乘理论的匹配算法,对路灯先验样本与候选路灯点云进行匹配筛选,并基于路灯骨架线信息构建的双重缓冲区,对候选路灯进行判别分析,实现路灯的提取和种类识别。试验表明,该算法对于遮挡少、数据相对完整的路灯提取准确度为95.2%,对于遮挡严重、点云密度低、数据完整性差的路灯提取准确度为78.0%,验证了该算法对大场景中路灯详细信息提取的稳健性。

The acquisition of detailed information for the streetlights in a large scene remains a tough task since the streetlights are of great number and types.In this paper,a method is proposed to extract and classify the streetlights,with the aid of prior sample sets on the basis of skeleton-line-buffer discriminant algorithm.First,a model and a priori sample set for streetlights are established according to the expression characteristics of streetlights in vehicle-borne LiDAR point cloud.Secondly,with the theory and method of mathematical morphology,the rod-shaped objects are extracted in vehicle LiDAR point cloud scene,and the candidate streetlights are chosen under the constraint of streetlight model and semantic rules.Then,the candidate samples are selected from the sample sets according to the parameter information and the statistical information obtained from the selected streetlights.Finally,based on the matching algorithm of least squares theory,we select and match the priori samples of streetlights and the candidate streetlights.Based on the double buffer of streetlight skeleton information,we discriminate and analyze the candidate streetlights to achieve the extraction and identification of street lights.Finally,the priori samples of streetlights and the point cloud of the candidate streetlights are matched and screened with the matching algorithm of least square theory;and based on the double buffer of streetlight skeleton information,the candidate streetlights are discriminated and analyzed to achieve the extraction and identification of streetlights.Our experiment shows that the algorithm is efficient and robust for the extraction of detailed information of streetlights.For the streetlights with less occlusion and relatively complete data,the extraction accuracy is 0.952,and for those with serous occlusion,low point cloud density and poor data integrity,the extraction accuracy is 0.780.And the above results validate the robustness of the proposed algorithm for the extraction of intermediate streetlights from large scenes.The detailed information extracted by the algorithm can be used to serve the fine and dynamic management of streetlights in large scenes.