Abstract
This paper presents a method for multiview range image registration to fuse 3D surfaces in range images taken from around an object by a laser range finder (LRF). The method uses competitive associative net (CAN2) for learning piecewise linear approximation of surfaces in the LRF range image involving various noise, and then executes pairwise registration of consecutive range images approximated by piecewise planes. To reduce the propagation error caused by the consecutive pairwise registration, the method introduces leave-one-image-out cross-validation (LOOCV) and tries to minimize the LOOCV registration error. The effectiveness is shown by using real LRF range images of several objects.
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References
Kurogi, S., Koya, H., Nagashima, R., Wakeyama, D., Nishida, T.: Range image registration using plane extraction by the CAN2. In: Proc. of CIRA (2009)
Kurogi, S., Nagi, T., Nishida, T.: Range image registration using particle filter and competitive associative nets. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part II. LNCS, vol. 6444, pp. 352–359. Springer, Heidelberg (2010)
Salvi, J., Matabosh, C., Foli, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing 25, 578–596 (2007)
Batlle, E., Matabosch, C., Salvi, J.: Summarizing image/surface registration for 6dof robot/camera pose estimation. In: MartÃ, J., BenedÃ, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 105–112. Springer, Heidelberg (2007)
Kurogi, S.: Plane extraction from range data using competitive associative nets. The Brain and Neural Networks 14(4), 273–281 (2007)
Kurogi, S., Sawa, M., Ueno, T., Fuchikawa, Y.: A batch learning method for competitive associative net and its application to function approximation. In: Proc. of SCI 2004, vol. 5, pp. 24–28 (2004)
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Kurogi, S., Nagi, T., Yoshinaga, S., Koya, H., Nishida, T. (2011). Multiview Range Image Registration Using Competitive Associative Net and Leave-One-Image-Out Cross-Validation Error. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_70
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DOI: https://doi.org/10.1007/978-3-642-24965-5_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
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