A novel algorithm for pose estimation based on generalized orthogonal iteration with uncertainty-weighted measuring error of feature points

• Published in: Journal of modern optics Vol. 65; no. 3; pp. 331 - 341
• Main Authors: Huo, Ju, Zhang, Guiyang, Cui, Jiashan, Yang, Ming
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 06.02.2018; Taylor & Francis Ltd
• Subjects: Algorithms; Computer simulation; Covariance matrix; Error analysis; generalized orthogonal iteration; Iterative algorithms; Objective function; Pose estimation; Stereo vision; Uncertainty; uncertainty of feature points; Vision systems; weighted measuring error
• ISSN: 0950-0340; 1362-3044
• DOI: 10.1080/09500340.2017.1397218

In this paper, we investigate the effect of anisotropic and correlated non-identical distributions of feature points' gray level on pose estimation in stereo vision system. The generalized orthogonal iteration (GOI) algorithm for pose estimation with uncertainty-weighted measuring error of feature target is proposed. In this method, the inverse covariance matrix is applied to describe the uncertainty of feature points, and weighted contribution of uncertainty to the error objective function is analysed. By transforming the uncertainty into the covariance-weighted data space, a novel objective function based on spatial collinear error is constructed. The orthogonal iterative algorithm is extended to stereo vision system for pose estimation and the GOI algorithm is deduced, by which the optimized solution to a novel objective function is given. Finally, simulation and actual experimental results show high accuracy and strong robustness of the proposed approach, and should therefore, have potential for a variety of engineering applications.