Globally Optimal Solution to the Generalized Relative Pose Estimation Problem using Affine Correspondences

• URL: http://arxiv.org/abs/2512.17188v1
• Date: Fri, 19 Dec 2025 03:10:14 GMT
• Title: Globally Optimal Solution to the Generalized Relative Pose Estimation Problem using Affine Correspondences
• Authors: Zhenbao Yu, Banglei Guan, Shunkun Liang, Zibin Liu, Yang Shang, Qifeng Yu,
• Abstract summary: Relative pose estimation using visual and inertial information has important applications in various fields.<n>We propose a globally optimal solver using affine correspondences to estimate the generalized relative pose with a known vertical direction.<n>The proposed solver is evaluated on synthetic data and real-world datasets.
• Score: 18.13747114612191
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile devices equipped with a multi-camera system and an inertial measurement unit (IMU) are widely used nowadays, such as self-driving cars. The task of relative pose estimation using visual and inertial information has important applications in various fields. To improve the accuracy of relative pose estimation of multi-camera systems, we propose a globally optimal solver using affine correspondences to estimate the generalized relative pose with a known vertical direction. First, a cost function about the relative rotation angle is established after decoupling the rotation matrix and translation vector, which minimizes the algebraic error of geometric constraints from affine correspondences. Then, the global optimization problem is converted into two polynomials with two unknowns based on the characteristic equation and its first derivative is zero. Finally, the relative rotation angle can be solved using the polynomial eigenvalue solver, and the translation vector can be obtained from the eigenvector. Besides, a new linear solution is proposed when the relative rotation is small. The proposed solver is evaluated on synthetic data and real-world datasets. The experiment results demonstrate that our method outperforms comparable state-of-the-art methods in accuracy.

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