Minimal Solvers for Relative Pose Estimation of Multi-Camera Systems using Affine Correspondences

Abstract

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs (a minimal number of one AC and one point correspondence) with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on three real-world scenes. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques. Source code is released at https://github.com/jizhaox/relative_pose_gcam_affine.