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 this https URL

本文提出了一种新方法，利用两个仿射对应关系来计算多相机系统的相对姿态，该方法可用于解决 6DOF 相关姿态估计问题，通过特殊参数化和几何约束之间的关系，使得所提出的求解器比现有算法更有效且更准确，并可用于解决其他各种相关姿态估计问题。

多摄像头系统间平移对应关系用于相对位姿估计

Affine Correspondences between Multi-Camera Systems for Relative Pose  Estimation

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
assumed. 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 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 real-world
scenes from the KITTI odometry benchmark. It is shown that the accuracy of the
estimated poses is superior to the state-of-the-art techniques.

提出了三种新的求解器，用于从仿射对应中估计多相机系统的相对位姿，使得用更少的对应数即可快速准确计算。

使用仿射对应关系计算广义相对位姿的最小案例

Minimal Cases for Computing the Generalized Relative Pose using Affine  Correspondences

A minimal solution using two affine correspondences is presented to estimate
the common focal length and the fundamental matrix between two semi-calibrated
cameras - known intrinsic parameters except a common focal length. To the best
of our knowledge, this problem is unsolved. The proposed approach extends point
correspondence-based techniques with linear constraints derived from local
affine transformations. The obtained multivariate polynomial system is
efficiently solved by the hidden-variable technique. Observing the geometry of
local affinities, we introduce novel conditions eliminating invalid roots. To
select the best one out of the remaining candidates, a root selection technique
is proposed outperforming the recent ones especially in case of high-level
noise. The proposed 2-point algorithm is validated on both synthetic data and
104 publicly available real image pairs. A Matlab implementation of the
proposed solution is included in the paper.

本文提出了一种仅使用两个仿射对应来估计两个半标定相机之间的公共焦距和基本矩阵的最小解法。其利用局部仿射变换导出的线性约束扩展了点对应技术，并通过隐变量技术高效地求解多元多项式系统。同时，为了选择出最优解，引入了新颖的条件和根选择技术，在高噪声情况下表现优异。该算法在合成数据和 104 对公开的实际图像上得到验证，论文中还包括了 MATLAB 实现。