Abstract
Estimation of camera pose from an image of n points or lines with known correspondence is a thoroughly studied problem in computer vision. Most solutions are iterative and depend on nonlinear optimization of some geometric constraint, either on the world coordinates or on the projections to the image plane. For real-time applications we are interested in linear or closed-form solutions free of initialization. We present a general framework which allows for a novel set of linear solutions to the pose estimation problem for both n points and n lines. We present a number of simulations which compare our results to two other recent linear algorithm as well as to iterative approaches. We conclude with tests on real imagery in an augmented reality setup. We also present an analysis of the sensitivity of our algorithms to image noise.
The authors are grateful for support through the following grants: NSF-IIS-0083209, NSF-EIA-0120565, NSF-IIS-0121293, NSF-EIA-9703220, a DARPA/ITO/NGI sub-contract to UNC, and a Penn Research Foundation grant.
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Ansar, A., Daniilidis, K. (2002). Linear Pose Estimation from Points or Lines. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_19
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DOI: https://doi.org/10.1007/3-540-47979-1_19
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