A scale stretch method based on ICP for 3D data registration
IEEE Transactions on automation science and engineering, 2009•ieeexplore.ieee.org
In this paper, we are concerned with the registration of two 3D data sets with large-scale
stretches and noises. First, by incorporating a scale factor into the standard iterative closest
point (ICP) algorithm, we formulate the registration into a constraint optimization problem
over a 7D nonlinear space. Then, we apply the singular value decomposition (SVD)
approach to iteratively solving such optimization problem. Finally, we establish a new ICP
algorithm, named Scale-ICP algorithm, for registration of the data sets with isotropic …
stretches and noises. First, by incorporating a scale factor into the standard iterative closest
point (ICP) algorithm, we formulate the registration into a constraint optimization problem
over a 7D nonlinear space. Then, we apply the singular value decomposition (SVD)
approach to iteratively solving such optimization problem. Finally, we establish a new ICP
algorithm, named Scale-ICP algorithm, for registration of the data sets with isotropic …
In this paper, we are concerned with the registration of two 3D data sets with large-scale stretches and noises. First, by incorporating a scale factor into the standard iterative closest point (ICP) algorithm, we formulate the registration into a constraint optimization problem over a 7D nonlinear space. Then, we apply the singular value decomposition (SVD) approach to iteratively solving such optimization problem. Finally, we establish a new ICP algorithm, named Scale-ICP algorithm, for registration of the data sets with isotropic stretches. In order to achieve global convergence for the proposed algorithm, we propose a way to select the initial registrations. To demonstrate the performance and efficiency of the proposed algorithm, we give several comparative experiments between Scale-ICP algorithm and the standard ICP algorithm.
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