基于车载激光点云的路灯提取方法  

A method of extracting streetlights based on vehicle-borne laser point clouds

作　　者：张富杰 王留召[2] 钟若飞[1] 许梦兵 靳欢欢 ZHANG Fujie;WANG Liuzhao;ZHONG Ruofei;XU Mengbing;JIN Huanhuan(College of Resource Environment and Tourism,Capital Normal University,Beijing 100048,China;Chinese Academy of Surveying&Mapping,Beijing 100039,China;Beijing GEO-Vision Tech.Co.,Ltd.,Beijing 100070,China)

机构地区：[1]首都师范大学资源环境与旅游学院,北京100048 [2]中国测绘科学研究院,北京100039 [3]北京四维远见信息技术有限公司,北京100070

出　　处：《测绘通报》2025年第3期46-51,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(42071444;U22A20568);国家重点研发计划(2022YFB3904101)。

摘　　要：路灯是城市的关键组成部件,及时准确地获取路灯信息在数字城市建设中至关重要。受限于城市环境复杂的地物结构和遮挡情况,传统的路灯提取方法仍存在精度不高、效率低和稳健性差等问题,且面对不同城市场景缺乏普适性。针对上述问题,本文提出了一种基于车载激光点云的城市路灯自动提取方法。首先,通过内部形状描述子(ISS)关键点建立圆柱空间邻域,利用密度阈值判别并反向投影获取潜在杆状物点集;然后,通过主成分分析法(PCA)主向量、法向量方向及夹角约束快速剔除行道树等非目标杆状物,得到候选路灯点集;最后,根据路灯点云的空间几何特征,通过随机森林算法构建决策树实例化模型对候选路灯进行匹配分类,实现路灯点云的精准提取。试验结果表明,面对规则独立分布或部分遮挡的路灯点云,本文方法具有良好的提取精度和稳健性,以及较强的实际应用价值。Streetlights are critical components of urban infrastructure,timely and accurate acquisition of streetlight information is essential for the development of digital cities.Constrained by the complex object structures and occlusions in urban environments,traditional streetlight extraction methods still suffer from low accuracy,inefficiency,and poor robustness.Additionally,these methods lack general applicability across different urban scenarios.To address these issues,this paper proposes an automatic method for extracting urban streetlights based on vehicle-borne laser point clouds.Firstly,a cylindrical spatial neighborhood is established using ISS keypoints,and potential pole-like objects are identified through density threshold discrimination and back-projection.Then,non-target pole-like objects,such as street trees,are rapidly eliminated using PCA principal vectors,normal vector directions,and angular constraints,resulting in a candidate set of streetlight points.Finally,leveraging the spatial geometric features of streetlight point clouds,a decision tree model is instantiated via a random forest algorithm to match and classify the candidate streetlights,achieving precise extraction of streetlight point clouds.Experimental results indicate that the proposed method attains high extraction accuracy and robustness when dealing with regularly distributed or partially occluded streetlight point clouds,demonstrating significant practical application value.

关 键 词：车载激光扫描 路灯点云 杆状特征 主向量 随机森林 

分 类 号：P237[天文地球—摄影测量与遥感]