Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation

TL;DR

This paper introduces a new minimal solver for estimating the relative pose of multi-camera systems using two affine correspondences, improving efficiency and accuracy over existing methods, and applicable to various pose estimation problems.

Contribution

The paper presents a novel minimal solver leveraging affine correspondences for multi-camera relative pose estimation, extending to problems with known rotation priors and outperforming state-of-the-art algorithms.

Findings

More efficient than existing algorithms

Achieves higher relative pose accuracy

Works with virtual and real multi-camera data

Abstract

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.