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

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/67322

Title: Automatic registration of MLS point clouds and SfM meshes of urban area
Authors: Yoshimura, Reiji Browse this author
Date, Hiroaki Browse this author
Kanai, Satoshi Browse this author
Honma, Ryohei Browse this author
Oda, Kazuo Browse this author
Ikeda, Tatsuya Browse this author
Keywords: Registration
MLS point clouds
SfM mesh
urban area
hash
similarity invariant feature
Issue Date: 2016
Journal Title: Geo-spatial Information Science
Volume: 19
Issue: 3
Start Page: 171
End Page: 181
Publisher DOI: 10.1080/10095020.2016.1212517
Abstract: Recent advances in 3D scanning technologies allow us to acquire accurate and dense 3D scan data of large-scale environments efficiently. Currently, there are various methods for acquiring large-scale 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 images. In 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 them. In general, SfM mesh has non-scale information; therefore, scale, position, and orientation of the SfM mesh are adjusted in the registration process. In our method, first, 2D feature points are extracted from both SfM mesh and MLS point cloud. This 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 edges. Then, the corresponding feature points between the MLS point cloud and the SfM mesh are searched efficiently, using similarity invariant features and hashing. Next, the coordinate transformation is applied to the SfM mesh so that the ground planes and corresponding feature points are adjusted. Finally, scaling Iterative Closest Point algorithm is applied for accurate registration. Experimental 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.
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in Geo-spatial Information Science on Geo-spatial information science, 2016 Vol.19, no. 3, 171–181, available online: http://www.tandfonline.com/10.1080/10095020.2016.1212517.
http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/67322
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 伊達 宏昭

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