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A large-scale multiview point cloud registration method based on distance statistical distribution of weak features

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Publicado en:Journal of Computational Design and Engineering vol. 12, no. 1 (Jan 2025), p. 312
Autor principal: Feng, Yun
Otros Autores: Tao, Guoren, Wu, Wenlei, Lin, Jiahao, Liu, Xiaojun, Chen, Liangzhou
Publicado:
Oxford University Press
Materias:
Stitching
Thresholds
Smoothness
Image registration
Engineering
Methods
Registration
Algorithms
Probability distribution
Optimization
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:There are issues such as poor layered smoothness, model distortion, and error accumulation in the process of large-scale weak feature point cloud stitching and registration. This paper proposes a precise point cloud registration method based on distance statistical distribution. By summarizing and statistically analyzing the distance thresholds during the iterative process, it accurately determines the closest points for point cloud registration, thus avoiding the problem of low registration accuracy caused by manually setting distance thresholds. By statistically analyzing the distance intervals of corresponding points in the point cloud to eliminate erroneous correspondences and utilizing pose graph optimization for global pose, this method ensures the smoothness and accuracy of point cloud registration. Experiments have validated the effectiveness of this method. Comparative experiments have demonstrated that this method surpasses traditional point cloud registration methods in terms of accuracy, convergence, and robustness.
ISSN:2288-5048
DOI:10.1093/jcde/qwaf001
Fuente:Engineering Database