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

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.

Research highlights
► We present a new algorithm for camera resection from as few as four correspondences.
► Our approach uses a rigorous probabilistic model for computing MAP estimates.
► Our algorithm also yields global uncertainty estimates (probability maps).
► The problem is shown to be partially linear, which leads to an efficient algorithm.
► Tests show that our method is more stable to Gaussian noise than related methods.