A Direct Least-Squares (DLS) method for PnP

Authors:
Joel A. Hesch;Stergios I. Roumeliotis
Affiliations:
University of Minnesota, Minneapolis, 55455, USA;University of Minnesota, Minneapolis, 55455, USA
Venue:
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Year:
2011

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Abstract

In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n = 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).