ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 97-103, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
06 Jun 2016
Jinhu Wang1,2, Roderik Lindenbergh1, Yueqian Shen1,3, and Massimo Menenti1 1Dept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, Post Box 5048, 2628CN Delft, the Netherlands
2Key Laboratory of Quantitative Remote Sensing Information Technology Academy of Opto-Electronics, Chinese Academy of Sciences No. 9 Deng Zhuang South Road, HaiDian District, 100094 Beijing, China
3School of Earth Science and Engineering, Hohai University No.1 Xikang Rd., 210098 Nanjing, China
Keywords: Laser scanning, Point clouds, Voxels, Clustering, Eigenvalues, Registration 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.
Conference paper (PDF, 5762 KB)

Citation: Wang, J., Lindenbergh, R., Shen, Y., and Menenti, M.: COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 97-103,, 2016.

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