Affine Correspondences between Multi-Camera Systems for Relative Pose
  Estimation
Banglei Guan Ji Zhao
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.