Robust registration algorithm based on rational quadratic kernel for point sets with outliers and noise
| Autor: | Shaoyi Du, Yang Jing, Yang Yang, Teng Wan, Ce Li, Wenting Cui, Runzhao Yao |
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| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Iterative closest point 020207 software engineering 02 engineering and technology Noise symbols.namesake Quadratic equation Hardware and Architecture Robustness (computer science) Kernel (statistics) Lagrange multiplier Singular value decomposition 0202 electrical engineering electronic engineering information engineering Media Technology symbols Algorithm Software Rigid transformation |
| Zdroj: | Multimedia Tools and Applications. 80:27925-27945 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | This paper proposes a new rigid registration algorithm based on the rational quadratic kernel to align point sets with outliers and noise. First of all, the multi-source point sets may contain a lot of outliers and noise and the traditional registration algorithm cannot handle the outliers and noise efficiently, this paper introduces the rational quadratic kernel to the rigid registration problem, which can resist outliers and suppress noise to improve the registration accuracy. Secondly, based on the new registration model, we present an iterative closest point (ICP) algorithm and use Lagrange multiplier and the singular value decomposition (SVD) to compute the rigid transformation. Moreover, the effect of the parameter is discussed detailly and a useful parameter control method is introduced to increase the accuracy and robustness of registration. A series of experiments on simulations and real data demonstrate that the proposed algorithm is more precise and robust than other algorithms. |
| Databáze: | OpenAIRE |
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