Abstract
This paper proposes a novel solution to the problem of pose estimation of three-dimensional objects using feature maps. Our approach relies on quaternions as the mathematical representation of object orientation. We introduce the rigid map, which is derived from Kohonen's self-organizing feature map. Its topology is fixed and chosen in accordance with the quaternion representation. The map is trained with computer-generated object views such that it responds to a preprocessed input image with one or more sets of object orientation parameters. Experimental results demonstrate that a pose estimate within the accuracy requirements can be found in more than 90% of all cases. Our current implementation operates at near frame rate on real input images.
now with the Signal Processing Lab of the Swiss Federal Institute of Technology in Lausanne, Switzerland
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© 1997 Springer-Verlag Berlin Heidelberg
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Winkler, S., Wunsch, P., Hirzinger, G. (1997). A feature map approach to pose estimation based on quaternions. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020275
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DOI: https://doi.org/10.1007/BFb0020275
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