Abstract
We present a probabilistic framework for matching of point clouds. Variants of the ICP algorithm typically pair points to points or points to lines. Instead, we pair data points to probability functions that are thought of having generated the data points. Then an energy function is derived from a maximum likelihood formulation. Each such distribution is a mixture of a bivariate Normal Distribution to capture the local structure of points and an explicit outlier term to achieve robustness. We apply our approach to the SLAM problem in robotics using a 2D laser range scanner.
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Besl, P.J., McKay, N.D.: A method for registration of 3d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Biber, P., Straßer, W.: The normal distributions transform: A new approach to laser scan matching. In: International Conference on Intelligent Robots and Systems, IROS (2003)
Black, M., Rangarajan, A.: On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. IJCV 19(1), 57–92 (1996)
Champleboux, G., Lavallee, S., Szeliski, R., Brunie, L.: From accurate range imaging sensor calibration to accurate model-based 3-d object localization. In: CVPR 1992, pp. 83–89 (1992)
Chen, Y., Medioni, G.G.: Object modeling by registration of multiple range images. Image and Vision Computing 10(3), 145–155 (1992)
Cox, I.J.: Blanche: An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation 7(2), 193–204 (1991)
Dennis, J.E., Schnabel, R.B.: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. SIAM Classics in Applied Mathematics (1996)
Dorai, C., Weng, J., Jain, A.K.: Optimal registration of object views using range data. IEEE TPAMI 19(10), 1131–1138 (1997)
Fitzgibbon, A.: Robust registration of 2d and 3d point sets. In: Proceedings of the British Machine Vision Conference, pp. 662–670 (2001)
Frese, U., Duckett, T.: A multigrid approach for accelerating relaxationbased slam. In: Proc. IJCAI Workshop on Reasoning with Uncertainty in Robotics, RUR 2003 (2003)
Gutmann, J.-S., Konolige, K.: Incremental mapping of large cyclic environments. In: Proceedings of the 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation (1999)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Learning 25(10) (October 2003)
Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2d range scans. In: CVPR 1994, pp. 935–938 (1994)
Lu, F., Milios, E.E.: Globally consistent range scan alignment for environment mapping. Autonomous Robots 4, 333–349 (1997)
Masuda, T., Yokoya, N.: A robust method for registration and segmentation of multiple range images. CVIU 61(3), 295–307 (1995)
Meer, P., Mintz, D., Rosenfeld, A., Kim, D.Y.: Robust regression methods for computer vision: A review. IJCV 6(1), 59–70 (1991)
Pulli, K.: Multiview registration for large data sets. In: Int. Conf. on 3D-DIM (1999)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proc. of the Third Intl. Conf. on 3D-Dim, pp. 145–152 (2001)
Stewart, C.: Robust parameter estimation in computer vision. SIAM Review 41(3), 512–537 (1999)
Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle adjustment – A modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–375. Springer, Heidelberg (2000)
Zhang, Z.: Iterative point matching for registration of free-from curves and surfaces. International Journal of Computer Vision 13(2), 119–152 (1994)
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Biber, P., Fleck, S., Strasser, W. (2004). A Probabilistic Framework for Robust and Accurate Matching of Point Clouds. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_59
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DOI: https://doi.org/10.1007/978-3-540-28649-3_59
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