Different Scale Point Cloud Registration based on Paired Constraint Matching of Line Vectors
Authors: 
Peng Yao, Jiye Qian, Ying HeAuthors Info & Claims
Pages 399 - 404
Abstract
Aiming at the accuracy and efficiency of point cloud registration at different scales, a new point cloud registration algorithm based on paired constraint matching of line vectors is proposed. Line vector is introduced to calculate the scale factor, and the line vector structure is used to group them. Then the triangular constraint is proposed, and the undirected graph is constructed by the correspondence of line vectors, and the corresponding relation of correct line vectors is matched by statistics of vertex degree of each undirected graph. Finally, the rotational translation of each group is estimated by iterated reweighted least square (IRLS). The experimental results show that compared with the current mainstream methods, the rotation accuracy is improved by 36.77% and the translation accuracy is improved by 62.82% in the homologous data set. In the heterogeneous data set, the rotation accuracy is improved by 87% and the translation accuracy is improved by 81.54%. The proposed algorithm has the ability of making full use of correspondence relationship and global optimization, and can realize multi-scale point cloud registration with high precision and fast.
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Index Terms
- Different Scale Point Cloud Registration based on Paired Constraint Matching of Line Vectors
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Published In
November 2023
1263 pages
ISBN:9798400708831
DOI:10.1145/3652628
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Published: 23 May 2024
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ICAICE 2023
ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering
November 17 - 19, 2023
Dalian, China
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