A objective function with measuring error uncertainty weighted for pose estimation in stereo vision

In order to investigate the effect of anisotropic and correlated non-identical gray distributions of feature points on pose estimation, a novel objective function with error uncertainty weighted of feature points was proposed. In the method, the inverse covariance matrix was utilized to describe the directional uncertainty of feature points, and weighted contribution of uncertainty to the error objective function was analysed. By transforming the uncertainty into a covariance-weighted data space, the novel objective function was constructed, which was suitable for actual applications. Finally, the optimized solution to the novel objective function was obtained via generalized orthogonal iterative algorithm. The simulation and practical experiments show that the maximum error of re-projection image coordinates of the target is less than 0.11 pixels and the measurement relative error for standard gauges is superior to 0.01% within the space 2300 mm×1400 mm×1400 mm. The results verify the high accuracy and strong robustness of the proposed approach, and should therefore have potential for engineering applications.