HYBRID CAMERA POSE ESTIMATION COMBINING SQUARE FIDUCIALS LOCALIZATION TECHNIQUE AND ORTHOGONAL ITERATION ALGORITHM

Abstract

Camera pose estimation from video images is a fundamental problem in machine vision and Augmented Reality (AR) systems. Most developed solutions are either linear for both n points and n lines, or iterative depending on nonlinear optimization of some geometric constraints. In this paper, we first survey several existing methods and compare their performances in an AR context. Then, we present a new linear algorithm which is based on square fiducials localization technique to give a closed-form solution to the pose estimation problem, free of any initialization. We also propose an hybrid technique which combines an iterative method, in fact the orthogonal iteration (OI) algorithm, with our own closed form solution. An evaluation of the methods has shown that this hybrid pose estimation technique is accurate and robust. Numerical experiments from real data are given comparing the performances of our hybrid method with several iterative techniques, and demonstrating the efficiency of our approach.