Reliable camera pose and calibration from a small set of point and line correspondences: A probabilistic approach

Authors:
Thomas Chaperon;Jacques Droulez;Guillaume Thibault
Affiliations:
Trimble 3D Scanning, 30 rue de la Fontaine du Vaisseau, 94120 Fontenay-sous-Bois, France;Laboratoire de Physiologie de la Perception et de l'Action, Collège de France, and UMR 7152, CNRS, 11 place Marcelin Berthelot, 75231 Paris Cedex 05, France;EDF R&D, 1 avenue du Géénéral de Gaulle, 92141 Clamart cedex, France
Venue:
Computer Vision and Image Understanding
Year:
2011

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Abstract

We present a new method for solving the problem of camera pose and calibration from a limited number of correspondences between noisy 2D and 3D features. We show that the probabilistic estimation problem can be expressed as a partially linear problem, where point and line correspondences are mixed using a common formulation. Our Sampling-Solving algorithm enables to robustly estimate the parameters and evaluate the probability distribution of the estimated parameters. It solves the problem of pose estimation with unknown focal length using a minimum of only four correspondences (five if the principal point is also unknown). To our knowledge, this is the first calibration method using so few correspondences of both points and lines. Experimental results on minimal data sets show that the algorithm is very robust to Gaussian noise. Experimental comparisons show that our method is much more stable than existing camera calibration methods for small data sets. Finally, some tests show the potential of global uncertainty estimates on real data sets.