Ni Yubo, Wang Xiangjun, Yin Lei
Appl Opt. 2019 Apr 10;58(11):2963-2972. doi: 10.1364/AO.58.002963.
This paper addresses the problem of relative pose estimation for multiple cameras in the context of motion-based camera calibration. Relative pose is found from a set of camera-target relative poses. A least-square kind loss function is established using 3D relative Riemannian metrics of targets and cameras. Lie algebra-based solvers are proposed for orientation and pose estimation. Multiple cameras are calibrated without common targets or features in space using our proposed framework. The simulation and real data evaluation demonstrate that our proposed algorithms can achieve high accuracy with limited and precise camera-target poses.
本文探讨了基于运动的相机校准背景下多相机相对位姿估计问题。相对位姿是从一组相机与目标的相对位姿中得出的。利用目标和相机的三维相对黎曼度量建立了一种最小二乘类损失函数。提出了基于李代数的求解器用于方向和位姿估计。使用我们提出的框架，无需空间中的公共目标或特征即可对多个相机进行校准。仿真和实际数据评估表明，我们提出的算法在有限且精确的相机与目标位姿情况下能够实现高精度。
