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Robust RGB-D Data Registration Based on Correntropy and Bi-directional Distance

机译:基于熵和双向距离的鲁棒RGB-D数据配准

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The iterative closest point (1CP) algorithm is most widely used for rigid registration of point sets. In this paper, a robust ICP registration method data is proposed to register RGB-D data. Firstly, the color information is introduced to build more precise correspondence between two point sets. Secondly, to enhance the robustness of the algorithm to noise and outliers, the maximum correntropy criterion (MCC) is introduced to the registration framework. Thirdly, to reduce the possibility of the algorithm falling into local minimum and deal with ill-pose issue, the bidirectional distance measurement is added to the proposed algorithm. Finally, the experimental results of point sets registration and scene reconstruction demonstrate that the proposed algorithm can obtain more precise and robust results than other ICP algorithms.
机译:迭代最近点(1CP)算法最广泛用于点集的刚性配准。本文提出了一种鲁棒的ICP配准方法数据来配准RGB-D数据。首先,引入颜色信息以建立两个点集之间更精确的对应关系。其次,为了提高算法对噪声和离群值的鲁棒性,将最大熵准则(MCC)引入到注册框架中。第三,为减少算法陷入局部最小值并解决不适问题的可能性,在该算法中增加了双向距离测量。最后,点集配准和场景重构的实验结果表明,与其他ICP算法相比,该算法可以获得更精确,更鲁棒的结果。

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