论文标题
使用已知$ \ MATHRM {SE}（3）$不变的校准相机的相对姿势估计
Relative Pose Estimation of Calibrated Cameras with Known $\mathrm{SE}(3)$ Invariants
论文作者
论文摘要
$ \ mathrm {se}（3）$不变的姿势包括其旋转角度和螺丝翻译。在本文中，我们介绍了一项完整的全面研究，对由已知$ \ mathrm {se}（3）（3）$不变的校准相机的相对姿势估计问题进行了完整的研究，该摄像机总共涉及5个最小问题。这些问题减少了相对姿势估计的点对数量最少，并提高了估计效率和鲁棒性。 $ \ mathrm {se}（3）$不变约束可能来自额外的传感器测量或运动假设。与传统的相对姿势估计不同，不需要额外的约束，不需要外部校准即可将约束转换为相机框架。这个优势来自$ \ mathrm {se}（3）$不变的不变性，使刚体上的不同坐标系统交叉，使求解器在实际应用中更方便，更灵活。 除了提出受$ \ mathrm {se}（3）$不变性限制的相对姿势估计的概念外，我们对现有多项式公式进行了全面研究，以进行相对姿势估计并发现其关系。为每个提出的问题仔细选择不同的配方，以达到最佳效率。与常规的相对姿势估计方法相比，合成和真实数据的实验显示了性能的提高。
The $\mathrm{SE}(3)$ invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total. These problems reduces the minimal number of point pairs for relative pose estimation and improves the estimation efficiency and robustness. The $\mathrm{SE}(3)$ invariant constraints can come from extra sensor measurements or motion assumption. Different from conventional relative pose estimation with extra constraints, no extrinsic calibration is required to transform the constraints to the camera frame. This advantage comes from the invariance of $\mathrm{SE}(3)$ invariants cross different coordinate systems on a rigid body and makes the solvers more convenient and flexible in practical applications. Besides proposing the concept of relative pose estimation constrained by $\mathrm{SE}(3)$ invariants, we present a comprehensive study of existing polynomial formulations for relative pose estimation and discover their relationship. Different formulations are carefully chosen for each proposed problems to achieve best efficiency. Experiments on synthetic and real data shows performance improvement compared to conventional relative pose estimation methods.
