Abstract
Mobile laser scanning can quickly and dynamically obtain a wide range of urban scene point clouds. However, due to factors such as occlusion and field of view limitation, it needs to be supplemented by terrestrial laser scanning. 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. To this end, a point cloud registration method of mobile and terrestrial scanning based on the target features of artificial ground objects is proposed. 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. 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. 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. 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. 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.
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This work was supported by the National key research and development program (2018YFB1600302); National Natural Science Foundation of China (42001414); and Shandong Provincial Natural Science Foundation, China(ZR2019BD033).
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Cong, B., Li, Q., Liu, R. et al. Research on a Point Cloud Registration Method of Mobile Laser Scanning and Terrestrial Laser Scanning. KSCE J Civ Eng 26, 5275–5290 (2022). https://doi.org/10.1007/s12205-022-0366-0
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DOI: https://doi.org/10.1007/s12205-022-0366-0