We present a novel method to compute the relative pose of multi-camera systems using two affine correspondences (ACs). Existing solutions to the multi-camera relative pose estimation are either restricted to special cases of motion, have too high computational complexity, or require too many point correspondences (PCs). Thus, these solvers impede an efficient or accurate relative pose estimation when applying RANSAC as a robust estimator. This paper shows that the 6DOF relative pose estimation problem using ACs permits a feasible minimal solution, when exploiting the geometric constraints between ACs and multi-camera systems using a special parameterization. We present a problem formulation based on two ACs that encompass two common types of ACs across two views, i.e., inter-camera and intra-camera. Moreover, the framework for generating the minimal solvers can be extended to solve various relative pose estimation problems, e.g., 5DOF relative pose estimation with known rotation angle prior. Experiments on both virtual and real multi-camera systems prove that the proposed solvers are more efficient than the state-of-the-art algorithms, while resulting in a better relative pose accuracy. Source code is available at https://github.com/jizhaox/relpose-mcs-depth.

翻译：我们提出了一种新颖的方法，利用两个仿射对应关系（AC）来计算多相机系统的相对位姿。现有的多相机相对位姿估计解决方案要么局限于运动特例，要么计算复杂度太高，要么需要过多的点对应关系（PC）。因此，当使用RANSAC作为鲁棒估计器时，这些求解器阻碍了高效或准确的相对位姿估计。本文证明，当利用AC与多相机系统之间的几何约束并采用特殊参数化方法时，基于AC的6自由度相对位姿估计问题存在可行的最小解。我们提出了一个基于两个AC的问题公式化，涵盖了跨两视图的两种常见AC类型，即相机间与相机内。此外，生成最小解器的框架可扩展到解决各种相对位姿估计问题，例如已知旋转角先验的5自由度相对位姿估计。在虚拟和真实多相机系统上的实验证明，所提出的求解器比现有最先进算法更高效，同时能获得更好的相对位姿精度。源代码可在https://github.com/jizhaox/relpose-mcs-depth获取。