Abstract
The iterative closest point (ICP) 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.
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References
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: using depth cameras for dense 3D modeling of indoor environments. Int. J. Robot. Res. 31(5), 647–663 (2013)
Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)
Bernard, F., et al.: Shape-aware surface reconstruction from sparse 3D point-clouds. Med. Image Anal. 38, 77–89 (2017)
P.J. Besl, N.D. Mckay: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell., 14(2), 239–256 (1992)
Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image Vis. Comput. 23(3), 299–309 (2005)
Ridene, T., Goulette, F.: Registration of fixed-and-mobile-based terrestrial Laser data sets with DSM. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 375–380 (2016)
Du, S., Liu, J., Zhang, C., Zhu, J., Li, K.: Probability iterative closest point algorithm for m-D point set registration with noise. Neurocomputing 157, 187–198 (2015)
Wu, Z., Chen, H., Du, S., Fu, M., Zhou, N., Zheng, N.: Correntropy based scale ICP algorithm for robust point set registration. Pattern Recogn. 93, 14–24 (2019)
Du, S., Xu, G., Zhang, S., Zhang, X., Gao, Y., Chen, B.: Robust rigid registration algorithm based on pointwise correspondence and correntropy. Pattern Recogn. Lett. (2018). https://doi.org/10.1016/j.patrec.2018.06.028
Fitzgibbon, A.W.: Robust registration of 2D and 3D point sets. Image Vis. Comput. 21(13), 1145–1153 (2001)
Benjemaa, R., Schmitt, F.: Fast global registration of 3D sampled surfaces using a multi-z-buffer technique. In: International Conference on Recent Advances in 3-D Digital Imaging and Modeling Proceedings, pp. 113–123 (1997)
Men, H., Gebre, B., Pochiraju, K.: Color point cloud registration with 4D ICP algorithm. In: IEEE International Conference on Robotics and Automation, pp. 1511–1516 (2011)
Danelljan, M., Meneghetti, G., Khan, F.S., Felsberg, M.: A probabilistic framework for color-based point set registration. In: Computer Vision and Pattern Recognition, pp. 1818–1826 (2016)
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: IEEE International Conference on Robotics and Automation, pp. 1817–1824 (2011)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE International Conference on Intelligent Robots and Systems, pp. 573–580 (2012)
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61971343, 61573274 and 61627811, Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) under Grant No. MJUKF-IPIC201802.
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Wan, T., Du, S., Cui, W., Xie, Q., Liu, Y., Li, Z. (2020). Robust RGB-D Data Registration Based on Correntropy and Bi-directional Distance. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_26
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DOI: https://doi.org/10.1007/978-3-030-37734-2_26
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