摘要
5G通信塔已成为人们生活中不可或缺的基础设施,对其进行数字化重建是高效管理和安全稳定监测的重要内容。针对其多站点激光扫描数据,研究提出一种基于改进旋转投影统计特征描述子(RoPS)和最大生成树的多视角配准方法,基于距离加权的半径滤波去除噪声点,采用改进RoPS算子与随机采样一致性方法完成点云粗配准,并采用ICP提高配准精度;然后计算点云重叠度并构建基于广度优先搜索的最大生成树,找出合适的配准姿态实现多站点云高精度全局配准;利用斯坦福数据集和通信塔地面三维激光扫描数据对该方法进行验证。结果表明:该方法配准精度达到了0.07 m。研究成果为通信塔三维模型重建和安全检测提供了可靠的技术和模型支持。
5G signal towers have become an indispensable infrastructure in people's lives,and their digital reconstruction is critical for safety monitoring and management.In this study,a novel method for multi-view point cloud registration based on improved Rotational Projection Statistical feature descriptors(RoPS)and maximum spanning tree is developed by taking advantage of the multi-site laser scan data.Prior to the coarse registration conducted by the improved RoPS descriptor and random sample consensus method,the noise was removed by applying the filter based on the distance-weighted radius.Iterative Closest Point(ICP)was employed for accuracy improvement and then calculating the overlap of point clouds and having the maximum spanning tree constructed based on the breadth-first search,a finer global registration combining points from multi-site was implemented at the proper pose.The Stanford dataset and ground 3D laser scanning data of signal towers were used for validation.The result shows that our method achieved 0.07 m in accuracy,indicating this reliable technique can support the 3D model reconstruction and safety monitoring of signal towers.
作者
徐麒
黎东
杜蒙
习晓环
王成
聂胜
Xu Qi;Li Dong;Du Meng;Xi Xiaohuan;Wang Cheng;Nie Sheng(Faculty of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,China;Key Lab of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China)
出处
《遥感技术与应用》
北大核心
2026年第1期235-241,共7页
Remote Sensing Technology and Application
基金
国家自然科学基金面上项目(42271365)。
关键词
通信塔
地面激光扫描
点云配准
多视角
最大生成树
Terrestrial laser scanning
Communication tower
Point cloud registration
Multi-view
Maximum spanning tree
