光学精密工程2025，Vol.33Issue(4)：579-590,12.DOI:10.37188/OPE.20253304.0579
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
摘要
Abstract
Addressing the issues of significant density variations,uneven spatial distribution,and indis-tinct features in UAV image point cloud and 3D laser point cloud within vegetation-covered and multi-slope 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.Ini-tially,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 elim-inating redundancies and overlapping points to achieve fusion.Ultimately,the asymptotic encrypted irregu-lar 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 reconstruc-tion,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Key words
Random Sample Consensus(RANSAC)/slope fitting/point cloud registration/Digital Ele-vation Model(DEM)/3D reconstruction分类
计算机与自动化引用本文复制引用
唐一亮,杨耘,万宇,徐永节,王锐,辜第桢..复杂坡面拟合的异源点云配准及三维地形精细重建[J].光学精密工程,2025,33(4):579-590,12.基金项目
陕西省教育厅服务地方专项计划项目(No.23JE002) （No.23JE002）
国家自然科学基金(No.42174032) （No.42174032）
