基于多特征约束的露天采场道路点云提取  

Extraction of Road Point Cloud in Open Pit Based on Multi feature Constraints

作　　者：毛亚纯[1] 杨哲玺 曹旺 齐迹 MAO Ya-chun;YANG Zhe-xi;CAO Wang;QI Ji(School of Resources&Civil Engineering,Northeastern University,Shenyang 110819,China;School of Geomatics&Geographic Sciences,Liaoning Technical University,Fuxin 123000,China)

机构地区：[1]东北大学资源与土木工程学院,辽宁沈阳110819 [2]辽宁工程技术大学测绘与地理科学学院,辽宁阜新123000

出　　处：《东北大学学报（自然科学版）》2024年第9期1326-1333,共8页Journal of Northeastern University(Natural Science)

基　　金：国家重点研发计划项目(2016YFCO801602);国家自然科学基金资助项目(52074064).

摘　　要：针对露天采场道路点云数据通过法向量、路缘石等点云特征难以准确提取的问题,提出了一种多特征约束的露天采场道路点云提取方法.以辽阳市千山石灰石矿露天采场激光点云为数据源,首先对原始数据进行降采样;然后基于单点RGB信息、邻域RGB信息、邻域高差、邻域粗糙度、反射强度5类点云特征,制作并划分了训练集和验证集,利用随机森林算法构建了道路点云提取模型并进行了优化,进一步引入欧式聚类算法改进了道路点云提取模型结果,最后评估了露天采场道路点云提取结果.结果表明,本文方法可以实时有效准确地提取露天采场道路点云数据.Aiming at the problem that road point cloud data in open pit is difficult to be accurately extracted through point cloud features such as normal vector and kerb,a method of road point cloud extraction in open pit with multi‑feature constraints was proposed.Taking the laser point cloud in the open pit of Qianshan limestone mine in Liaoyang City as the data source,the original data was downsampled firstly,and then the training set and verification set were made and divided based on the five kinds of point cloud features including single point RGB information,neighborhood RGB information,neighborhood height difference,neighborhood roughness,and reflection intensity.The road point cloud extraction model was constructed and optimized using the random forest algorithm.Furthermore,European clustering algorithm was introduced to improve the road point cloud extraction model.Finally,the road point cloud extraction results were evaluated in open pit.The results show that the proposed method can effectively and accurately extract the road point cloud data in open pit in real time.

关 键 词：露天采场 道路点云 点云特征信息 随机森林算法 欧式聚类算法 

分 类 号：P231[天文地球—摄影测量与遥感]