This paper presents a solution to the problem of pose estimation in the presence of heavy radial distortion and a potentially large number of outliers. The main contribution is an algorithm that solves for radial distortion, focal length and camera pose using a minimal set of four point correspondences between 3D world points and image points. We use a RANSAC loop to find a set of inliers and an initial estimate for bundle adjustment. Unlike previous approaches where one starts out by assuming a linear projection model, our minimal solver allows us to handle large radial distortions already at the RANSAC stage. We demonstrate that with the inclusion of radial distortion in an early stage of the process, a broader variety of cameras can be handled than was previously possible. In the experiments, no calibration whatsoever is applied to the camera. Instead we assume square pixels, zero skew and centered principal point. Although these assumptions are not strictly true, we show that good results are still obtained and by that conclude that the proposed method is applicable to uncalibrated photographs.
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