International Conference on Communications. Guo YL, Sohel FA, Bennamoun M, Wan JW, Lu M (2013) RoPS: A local feature descriptor for 3D rigid objects based on rotational projection statistics. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia 117–122, DOI: Gressin A, Cannelle B, Mallet C, Papelard JP (2012) Trajectory based registration of 3D LiDAR point clouds acquired with a mobile mapping system.

International Archives of Photogrammetry & Remote Sensing 36:25–27, DOI: IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2):239–256, DOI: ĭold C, Brenner C (2006) Registration of terrestrial laser scanning data using planar patches and image data. Computer Engineering and Applications 46(23):1–7īesl PJ, Mckay ND (1992) A method for registration of 3-D shapes.

Experiments show that the point cloud registration accuracy can reach 2.6 cm, which provides a feasible method for high precision fusion of multi-platform laser point clouds.īao L, Cheng Z, Dang G (2010) Study on normal estimation for 3D point clouds. Finally, the key point constraint is used to improve the deficiencies of 4PCS (4-Points Congruent Sets) algorithm and ICP (Iterative Closest Point) algorithm to complete the registration of mobile and terrestrial point clouds in different road scenes. Combined with the octree voxel index, the multi-scale key point extraction method is constructed to extract the multi-scale key points of registration primitives. Then, the artificial ground objects are extracted as the registration primitives to reduce the scene complexity, and the features of urban scenes and the features of point cloud eigenvalues and principal curvature attributes are analyzed. Firstly, the data features of mobile laser scanning point clouds and terrestrial laser scanning point clouds are analyzed, and the point clouds are diluted with equal density. To this end, a point cloud registration method of mobile and terrestrial scanning based on the target features of artificial ground objects is proposed. The acquisition methods and data quality of mobile point clouds and terrestrial point clouds are quite different, the target of urban scene point clouds is complex and diverse, and the corresponding feature is difficult to extract, so the point cloud fusion is difficult. However, due to factors such as occlusion and field of view limitation, it needs to be supplemented by terrestrial laser scanning. Mobile laser scanning can quickly and dynamically obtain a wide range of urban scene point clouds.

Point cloud data