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Volume 6 Issue 4
Jul.  2019

IEEE/CAA Journal of Automatica Sinica

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Hongchen Chen, Xie Zhang, Shaoyi Du, Zongze Wu and Nanning Zheng, "A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 981-991, July 2019. doi: 10.1109/JAS.2019.1911579
Citation: Hongchen Chen, Xie Zhang, Shaoyi Du, Zongze Wu and Nanning Zheng, "A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration," IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 981-991, July 2019. doi: 10.1109/JAS.2019.1911579

A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration

doi: 10.1109/JAS.2019.1911579
Funds:  This work was supported in part by the National Natural Science Foundation of China (61627811, 61573274, 61673126, U1701261)
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  • The iterative closest point (ICP) algorithm has the advantages of high accuracy and fast speed for point set registration, but it performs poorly when the point set has a large number of noisy outliers. To solve this problem, we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers. Firstly, we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model, which can avoid the influence of outliers. To maximize the objective function, we then propose a robust affine ICP algorithm. At each iteration of this new algorithm, we set up the index mapping of two point sets according to the known transformation, and then compute the closed-form solution of the new transformation according to the known index mapping. Similar to the traditional ICP algorithm, our algorithm converges to a local maximum monotonously for any given initial value. Finally, the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.

     

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    Highlights

    • Correntropy is introduced to our algorithm to suppress the outliers and noises.
    • The one-to-one index mapping is employed for fast speed.
    • The closed-form solution of our algorithm is presented to reduce the run-time.
    • Our algorithm is independent of feature extraction and can be used with other methods.

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