Abstract
In this paper, we describe a method for finding the pose of an object from a single image. We assume that we can detect and match in the image three feature points of the object, and that we know their relative geometry on the object. At first we present the exact pose calculation with an existing method and emphasize the limitation. Then we introduce a new method which consists of adding to the camera an inclinometer so that we reduce the number of unknown parameters and thus are able to compute the pose efficiently by using a classical iterative optimization method.
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References
DeMenthon, D., Davis, L.S.: Model-based object pose in 25 lines of code. In: European Conference on Computer Vision, pp. 335–343 (1992)
DeMenthon, D., Davis, L.S.: Exact and approximate solutions of the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 14(11), 1100–1105 (1992)
Lu, C.P., Hager, G.D., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 610–622 (2000)
Lee, P.Y., Moore, J.B.: Pose estimation via gauss-newton-on-manifold. In: Proceedings of the Sixth International Symposium on Mathematical Theory of Networks and Systems, Pacific Grove, California, USA, pp. 131–135 (2004)
Or, S.H., Luk, W.S., Wong, K.H., King, I.: An efficient iterative pose estimation algorithm. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352(2), pp. 559–566. Springer, Heidelberg (1997)
Lowe, D.G.: Fitting parameterized three-dimensional models to images. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-13(5), 441–450 (1991)
Quan, L., Lan, Z.D.: Linear n-point camera pose determination. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 774–780 (1999)
Fabrizio, J., Devars, J.: The perspective-n-point problem for catadioptric sensors: An analytical approach. In: International Conference on Computer Vision and Graphics (ICCVG’04), Warsaw, Poland (2004)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Breuel, T.M.: An efficient correspondence based algorithm for 2D and 3D model based recognition. Technical Report AIM-1259 (1990)
Lowe, D.G.: Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence 31(3), 355–395 (1987)
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Merckel, L., Nishida, T. (2007). Solution of the Perspective-Three-Point Problem. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_32
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DOI: https://doi.org/10.1007/978-3-540-73325-6_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
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