Affine Correspondences Between Multi-Camera Systems for Relative Pose Estimation

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, we exploit a unified and versatile framework for generating 6DOF solvers. Building upon this foundation, we use this framework to address two categories of practical scenarios. First, for the more challenging 7DOF relative pose estimation problem-where the scale transformation of multi-camera systems is unknown-we propose 7DOF solvers to compute the relative pose and scale using three ACs. Second, leveraging inertial measurement units (IMUs), we introduce several minimal solvers for constrained relative pose estimation problems. These include 5DOF solvers with known relative rotation angle, and 4DOF solver with known vertical direction. 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.