Print Email Facebook Twitter Coarse point cloud registration by EGI matching of voxel clusters Title Coarse point cloud registration by EGI matching of voxel clusters Author Wang, J. (TU Delft Optical and Laser Remote Sensing; Chinese Academy of Sciences) Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing) Shen, Y. (TU Delft Optical and Laser Remote Sensing; Hohai University) Menenti, M. (TU Delft Optical and Laser Remote Sensing) Date 2016 Abstract Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration. Subject Laser scanningPoint cloudsVoxelsClusteringEigenvaluesRegistration To reference this document use: http://resolver.tudelft.nl/uuid:98d5dae7-1bbc-4237-9668-2c631812f4e1 DOI https://doi.org/10.5194/isprs-annals-III-5-97-2016 ISSN 2194-9042 Source ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, III (5), 97-103 Event XXIII ISPRS Congress, 2016-07-12 → 2016-07-19, Prague, Czech Republic Part of collection Institutional Repository Document type journal article Rights © 2016 J. Wang, R.C. Lindenbergh, Y. Shen, M. Menenti Files PDF isprs_annals_III_5_97_2016.pdf 5.71 MB Close viewer /islandora/object/uuid:98d5dae7-1bbc-4237-9668-2c631812f4e1/datastream/OBJ/view