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Zhiyong Liu

at Beijing Institute of Technology

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Proceedings Article | 9 October 2021 Poster + Paper

Point cloud registration algorithm based on curvature and direction vector threshold

Zhiyong Liu, Qun Hao, Yao Hu, Shaohui Zhang

Proceedings Volume 11897, 118970S (2021) https://doi.org/10.1117/12.2601319

KEYWORDS: Data processing, Clouds, Data modeling, Principal component analysis, Image registration, 3D modeling, Reverse engineering, Reliability, Machine vision, Computer vision technology

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With the rapid development of computer vision, archaeology, medicine, reverse engineering and other fields, optical 3D measurement, as one of its crucial technologies, has been utilized more and more widely. In actual measurement, due to the limitation of the measurement range of the measuring equipment and the occlusion of the measured object, it is difficult to obtain the complete shape of the object through single measurement, thus requires multiple measurement from different perspectives and registration of the point cloud data obtained from each perspective together. To realize the registration and stitching of two point clouds with relative low overlap rate, this paper proposes a method based on curvature features and direction vector threshold. In the registration step, the curvature feature of the point cloud data is utilized to achieve accurate matching, and the Kdtree nearest neighbor search method is used to improve the matching points searching speed. In order to further reduce the registration error, the wrong point pairs are eliminated with the direction vector threshold method. The OpenMP multi-threaded parallel calculation method is used in the process of calculating the direction vector to improve the efficiency and speed. Subsequently, the rotation matrix R and the translation vector t between two point clouds are obtained by singular value decomposition method. Finally, the obtained transformation matrix is used to realize the rigid body transformation between the point clouds. Experimental results show that the proposed algorithm can effectively improves the registration accuracy and time efficiency of point cloud data with low initial overlap rate.

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