The problem of 3D3D registration entails the estimation of spatial transformation which best aligns two point sets. Iterative Closest PointIterative Closest Point (ICP) is arguably the most popular and one of the most effective algorithms for 3D3D registration at present. This algorithm uses singular value decompositionDecomposition to obtain a least squares alignment of two point sets. As a greedy alignment procedure, Iterative Closest PointIterative Closest Point (ICP) is liable to converge to sub-optimal solutions. In this study, the problem of 3D3D registration is addressed using the popular Bees AlgorithmBees AlgorithmmetaheuristicsMetaheuristics. Thanks to its global searchGlobal Searchapproach, the Bees AlgorithmBees Algorithm, THE is known to be highly impervious to sub-optimal convergence. To increase the efficiency of the search, singular value decompositionDecomposition is used to exploit the search results of the BeesBees Algorithm. Experimental evidence showed that the proposed algorithm outperformed Iterative Closest PointIterative Closest Point (ICP) in terms of consistency and precision and showed high robustness to noiseNoise in the point sets.


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    Title :

    Global Optimisation for Point Cloud Registration with the Bees Algorithm


    Additional title:

    Springer Ser.Advanced Manufacturing


    Contributors:


    Publication date :

    2022-11-20


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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