Abstract

We propose a real-time, robust to outliers and accuratesolution to the Perspective-n-Point (PnP) problem. Themain advantages of our solution are twofold: first, it in-tegrates the outlier rejection within the pose estimationpipeline with a negligible computational overhead; and sec-ond, its scalability to arbitrarily large number of correspondences. Given a set of 3D-to-2D matches, we formulatepose estimation problem as a low-rank homogeneous system where the solution lies on its 1D null space. Outliercorrespondences are those rows of the linear system which perturb the null space and are progressively detected by projecting them on an iteratively estimated solution of thenull space. Since our outlier removal process is based on an algebraic criterion which does not require computing thefull-pose and reprojecting back all 3D points on the imageplane at each step, we achieve speed gains of more than100× compared to RANSAC strategies. An extensive experimental evaluation will show that our solution yields accurate results in situations with up to 50% of outliers, and can process more than 1000 correspondences in less than 5ms.