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Automatic registration of MLS point clouds and SfM meshes of urban area

Geo-Spatial Information Science 2016年第3期19卷 171-181页

作者： Reiji Yoshimura Hiroaki Date Satoshi Kanai Ryohei Honma Kazuo Oda Tatsuya Ikeda Graduate School of Information Science and Technology Hokkaido UniversitySapporoJapan Asia Air Survey Co. LtdKawasakiJapan

Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments ***,there are various methods for acquiring largescale 3D scan data,such as Mobile Laser Scanning(MLS),Airborne Laser Scanning,Terrestrial Laser Scanning,photogrammetry and Structure from Motion(SfM).Especially,MLS is useful to acquire dense point clouds of road and road-side objects,and SfM is a powerful technique to reconstruct meshes with textures from a set of digital *** this research,a registration method of point clouds from vehicle-based MLS(MLS point cloud),and textured meshes from the SfM of aerial photographs(SfM mesh),is proposed for creating high-quality surface models of urban areas by combining *** general,SfM mesh has non-scale information;therefore,scale,position,and orientation of the SfM mesh are adjusted in the registration *** our method,first,2D feature points are extracted from both SfM mesh and MLS point *** process consists of ground-and building-plane extraction by region growing,random sample consensus and least square method,vertical edge extraction by detecting intersections between the planes,and feature point extraction by intersection tests between the ground plane and the ***,the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently,using similarity invariant features and ***,the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are ***,scaling Iterative Closest Point algorithm is applied for accurate *** results for three data-sets show that our method is effective for the registration of SfM mesh and MLS point cloud of urban areas including buildings.

关键词： Registration MLS point clouds SfM mesh urban area hash similarity invariant feature

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