Automatic registration of large-scale urban scene point clouds based on semantic feature points

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 113; pp. 43 - 58
• Main Authors: Yang, Bisheng, Dong, Zhen, Liang, Fuxun, Liu, Yuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-03-2016
• Subjects: Automation; Categories; Extraction; Feature extraction; Laser scanning point clouds; Multi-view registration; Point cloud segmentation; Segmentation; Semantic feature point extraction; Semantics; Three dimensional models; Urban environments; Urban scene mapping; Semantic feature point extraction; Point cloud segmentation; Urban scene mapping; Multi-view registration; Laser scanning point clouds
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2015.12.005

Point clouds collected by terrestrial laser scanning (TLS) from large-scale urban scenes contain a wide variety of objects (buildings, cars, pole-like objects, and others) with symmetric and incomplete structures, and relatively low-textured surfaces, all of which pose great challenges for automatic registration between scans. To address the challenges, this paper proposes a registration method to provide marker-free and multi-view registration based on the semantic feature points extracted. First, the method detects the semantic feature points within a detection scheme, which includes point cloud segmentation, vertical feature lines extraction and semantic information calculation and finally takes the intersections of these lines with the ground as the semantic feature points. Second, the proposed method matches the semantic feature points using geometrical constraints (3-point scheme) as well as semantic information (category and direction), resulting in exhaustive pairwise registration between scans. Finally, the proposed method implements multi-view registration by constructing a minimum spanning tree of the fully connected graph derived from exhaustive pairwise registration. Experiments have demonstrated that the proposed method performs well in various urban environments and indoor scenes with the accuracy at the centimeter level and improves the efficiency, robustness, and accuracy of registration in comparison with the feature plane-based methods.