Insights into the robustness of control point configurations for homography and planar pose estimation
This work addresses the problem of improving pose estimation accuracy for applications like fiducial markers, but it is incremental as it builds on existing methods by optimizing control point configurations.
The paper investigates how the spatial arrangement of control points affects the accuracy and robustness of homography and planar pose estimation methods, finding that optimized configurations improve performance, with empirical verification showing enhanced results for algorithms like DLT, IPPE, EPnP, and iterative reprojection error minimization.
In this paper, we investigate the influence of the spatial configuration of a number of $n \geq 4$ control points on the accuracy and robustness of space resection methods, e.g. used by a fiducial marker for pose estimation. We find robust configurations of control points by minimizing the first order perturbed solution of the DLT algorithm which is equivalent to minimizing the condition number of the data matrix. An empirical statistical evaluation is presented verifying that these optimized control point configurations not only increase the performance of the DLT homography estimation but also improve the performance of planar pose estimation methods like IPPE and EPnP, including the iterative minimization of the reprojection error which is the most accurate algorithm. We provide the characteristics of stable control point configurations for real-world noisy camera data that are practically independent on the camera pose and form certain symmetric patterns dependent on the number of points. Finally, we present a comparison of optimized configuration versus the number of control points.