Title
Using 2-Lines Congruent Sets for Coarse Registration of Terrestrial Point Clouds in Urban Scenes
Abstract
The registration of terrestrial point clouds is a crucial technique to obtaining the entire scene geometry. However, urban scenes, characterized by repetitive components, symmetrical structures, and object occlusion, pose ambiguity challenge of matches in the accurate registration of terrestrial point clouds. This article proposes a coarse registration approach using 2-lines congruent sets (2LCS) for urban point clouds. 3-D line features are extracted in the point clouds and classified into two categories according to directions. With that, a pair of 3-D line features from different categories is selected in the target point cloud to construct the 2-lines base with significant geometric characteristics. Next, the congruent 2-lines bases are identified in the source point cloud using geometric constraints, leading to the 2LCS. The 2LCS are then used to estimate the rigid motion between pairwise point cloud, leading to a set of candidate transformation parameters. The largest common point (LCP) set and maximum line structure consistency (MLSC) are integrated to evaluate the transformations. Finally, the procedure is repeated based on the RANSAC scheme to achieve optimal transformation values. We applied the proposed method to nine urban scenes with varying conditions and compared its performance with other state-of-the-art methods. The results show that our proposed approach is more robust for registering terrestrial point clouds in urban scenes with repetitive components, symmetrical structures, and limited overlap, leading to optimal or near-optimal results after fine registration optimization.
Year
DOI
Venue
2022
10.1109/TGRS.2021.3128403
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Feature extraction, Three-dimensional displays, Registers, Buildings, Deep learning, Probabilistic logic, Geometry, 3-D line feature, point cloud registration, terrestrial laser scanner (TLS), urban scenes
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ershuai Xu121.39
Zhihua Xu2397.64
Keming Yang301.35