The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.
Point set registration is a key method in computer vision and pattern recognition. In this paper, the correntropy and bi-directional distance are introduced into registration framework and a new robust registration model for RGB-D data is proposed. Firstly, as registering point sets with smooth structure, such as surface or plane, is easy failed, the color and position information is fused to establish more precise correspondence between two RGB-D data sets. Secondly, to reduce the influence of noises and eliminate outliers, the registration model based on the maximum correntropy criterion is established. Thirdly, the bi-directional distance measurement is introduced into the registration framework to avoid the model being trapped into local extremum. In addition, to solve this new registration problem, a new iterative closest point (ICP) algorithm is proposed, which converges to the local optimal solution by iterations. In the experiments, the proposed algorithm achieves more robustness and precise registration results than other algorithms.
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