Abstract
Be confronted with the challenges of efficiency and accuracy, point cloud registration, as a universal technique adopted in vision system, has always been used for large-dimension workpieces measurement. In this paper, we present a matching algorithm for determining the transformation relation between local point cloud and global point cloud with corresponding points unknown. First, multilinked lists of distance for point clouds are constructed with k-D tree. Then, a closed-traversal matching algorithm is proposed, which uses subgraph isomorphism to find possible matching results. The possible results still need further verification by recursive to get a credible matching result. In the end, a method is designed to solve and verify the transformation matrix by singular value decomposition. The performance of the algorithm is evaluated with the actual data obtained from vision measuring system. The experiments show that the algorithm is of high performance and efficiency and can be applied to practical problems of point cloud registration.
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This research was partially supported by the key research project of the Ministry of Science and Technology (Grant No. 2017YFB1301503) and the National Natural Science Foundation of China (Grant No. 51975344).
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Wu, Z., Yang, Y., Zhang, X., Zhang, L. (2020). A Matching Algorithm for Featureless Sparse Point Cloud Registration. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_2
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DOI: https://doi.org/10.1007/978-3-030-66645-3_2
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