论文标题
全球最佳的相对姿势估计与重力先验
Globally Optimal Relative Pose Estimation with Gravity Prior
论文作者
论文摘要
使用的智能手机，平板电脑和相机系统，例如，在汽车和无人机中，通常配备可以准确测量重力矢量的IMU（惯性测量单元）。使用这些其他信息，可以对摄像机的$ y $轴对齐，从而将它们的相对取向降低到单个自由度。通过此假设，我们提出了一个新型的全球最佳求解器，在最小二乘中最小化代数误差，以估计过度确定的情况下的相对姿势。基于表极约束，我们将优化问题转换为仅使用两个未知数来求解两个多项式。同样，使用旋转的一阶近似提出了快速求解器。将提出的求解器与四个现实世界数据集上的最先进的求解器进行了比较。总共50000图。此外，我们通过智能手机收集了一个数据集，该数据集由10933个图像对，重力方向和地面真相3D重建组成。
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the $y$-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least-squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions, and ground truth 3D reconstructions.
