Globally Optimal Solution to the Generalized Relative Pose Estimation Problem using Affine Correspondences
This work addresses the need for accurate pose estimation in applications like self-driving cars, but it is incremental as it builds on existing methods with specific optimizations.
The paper tackles the problem of relative pose estimation for multi-camera systems with known vertical direction by proposing a globally optimal solver using affine correspondences, achieving improved accuracy over state-of-the-art methods as demonstrated on synthetic and real-world datasets.
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.