复杂坡面拟合的异源点云配准及三维地形精细重建  

Registration of heterogeneous point cloud and precise reconstruction of 3D terrain via complex slope surface fitting

作　　者：唐一亮 杨耘[1,2] 万宇 徐永节 王锐 辜第桢 TANG Yiliang;YANG Yun;WAN Yu;XU Yongjie;WANG Rui;GU Dizheng(College of Geological Engineering and Geomatics,Chang'an University,Xi'an 710054,China;Cooperation Department of Chang'an University,State Key Laboratory of Geographic Information Engineering,Xi'an 710054,China)

机构地区：[1]长安大学地质工程与测绘学院,陕西西安710054 [2]地理信息工程国家重点实验室长安大学合作部,陕西西安710054

出　　处：《光学精密工程》2025年第4期579-590,共12页Optics and Precision Engineering

基　　金：陕西省教育厅服务地方专项计划项目(No.23JE002);国家自然科学基金(No.42174032)。

摘　　要：针对植被覆盖、地形起伏较大区域的无人机影像点云和三维激光点云密度差异大、空间分布不均匀以及特征不明显等现象,提出了一种点云数据关键坡面特征增强的采样一致性初始配准(Sample Consensus Initial Alignment,SACIA)与迭代最邻近点(Iterative Closest Point,ICP)组合配准算法。首先,分别对两源点云数据进行预处理,再基于随机抽样一致(Random Sample Consensus,RANSAC)算法对预处理后面特征弱的点云区域进行拟合,增强点云的面特征并构建多个关键坡面;进而,组合SAC-IA与ICP算法对两源点云进行配准,再进行冗余和重叠点消除处理,实现了两源点云的融合;最后,选取渐近加密不规则三角网滤波(Progressive Triangulated Irregular Network,PTIN)算法对融合后的点云提取地面点,利用反距离加权(Inverse Distance Weighted,IDW)算法进行三维地形重建,生成数字高程模型(Digital Elevation Model,DEM)。利用实测数据进行验证表明:与传统的SAC-IA与ICP组合算法相比,本文算法配准后的点云数据精度(均方根误差值)降低了3.325 m;利用融合后点云数据重建的DEM点位精度(用平均绝对误差和均方根误差表示)值分别降低了0.18 m和0.14 m。本文算法生成的DEM满足1:500比例尺国家规范要求,DEM更能反映地形细部特征。Addressing the issues of significant density variations,uneven spatial distribution,and indistinct features in UAV image point cloud and 3D laser point cloud within vegetation-covered and multislope regions,this study introduced a novel algorithm that combined sampling consistency initial alignment(SAC-IA)and iterative closest point(ICP)methods to enhance key slope features in point cloud data.Initially,preprocessing was performed on both source point cloud datasets,followed by the application of the random sample consensus(RANSAC)algorithm to fit the post-preprocessing point cloud regions with weak features,thereby enhancing the surface features and establishing multiple key slopes.Subsequently,the SAC-IA and ICP algorithms were integrated to register the two-source point cloud,subsequently eliminating redundancies and overlapping points to achieve fusion.Ultimately,the asymptotic encrypted irregular triangulation network(PTIN)filtering algorithm was employed to extract ground points from the fused point cloud,while the inverse distance weighting(IDW)algorithm was utilized for 3D terrain reconstruction,resulting in the generation of a digital elevation model(DEM).Validation using actual measurement data demonstrates that,compared to the traditional SAC-IA and ICP combined algorithm,the point cloud data accuracy represented by root mean square error value after registration of the algorithm in this paper is reduced by 3.325 m;The DEM point accuracy(represented by mean absolute error and root mean square error)reconstructed from the fused point cloud data decreased by 0.18 m and 0.14 m respectively.The DEM generated by this study's algorithm meets the national specification requirements for a 1:500 scale,and it more accurately reflects topographic details.

关 键 词：随机抽样一致 坡面拟合 点云配准 数字高程模型 三维重建 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术]