Abstract
The present study primarily discusses the perturbance error indeterminacy that is caused by anisotropic and correlated non-identical gray distribution of feature points in vision measurement for space target pose parameters. On that basis, an expedited algorithm that involves perturbance affine term based on the novel statistical objective function is proposed. By invoking the inverse covariance matrix to model a novel data space, the pose estimation algorithm based on projection vector is capable of reducing the effect of different levels of disturbance error on the measured results, as well as effectively avoiding the poor or non-convergence attributed to data degradation. Furthermore, the repeated calculation is avoided by coupling each iteration, which significantly simplifies the computation. As a consequence, the calculation complexity of each iteration decreases from O(n) to O(1), and the expediting process is implemented significantly. Lastly, as revealed from the experimental results, the calculation efficiency is improved by 3.3 times, and the maximum measured error of the space target attitude is less than 0.1°. Compared with the conventional methods, the proposed algorithm exhibits the effectively promoted speed-ability, precision and indeterminacy attenuation performance, suggesting that the proposed approach should have promising practical applications in deep-space target capture.
Highlights
Pose estimation is the problem to solve the target position and attitude under required conditions, i.e., the known internal parameters of the camera, the set spatial point coordinates, as well as the corresponding planar image point coordinates[1],[2]
Pose estimation is termed as the multi-point perspective problem, which has been prevalently employed in photogrammetry, robotics and industrial inspection[3]-[5]
The target motion parameters are measured by the EIPAT algorithm, and the motion trajectory of the turntable space vehicle is a sine curve with the amplitudes rising from 0 to 35
Summary
Pose estimation is the problem to solve the target position and attitude under required conditions, i.e., the known internal parameters of the camera, the set spatial point coordinates, as well as the corresponding planar image point coordinates[1],[2]. [8],[9], proposed the nonlinear iterative algorithm with a scale coefficient, which exhibits high calculation accuracy and good robustness and meets the orthogonality constraint This method should still meet high initial value requirements, and it remains low in practical applicability. Hartley, et al.[11] used the traversing search space and the branch constraint to develop the global optimal solution of infinite norm; the calculation is significantly huge. These existing classic methods are the suboptimal solutions in a specific limited domain for the complex requirements of measurement scenarios.
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