张宁 Visual Odometry Using a Homography Formulation with Decoupled Rotation and Translation Estimation Using Minimal Solutions
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视觉测距法使用单应性配方，使用最小解决方案进行解耦旋转和平移估计
Abstract— In this paper we present minimal solutions for two-view relative motion estimation based on a homography formulation. By assuming a known vertical direction (e.g.from an IMU) and assuming a dominant ground plane we
demonstrate that rotation and translation estimation can be decoupled. This result allows us to reduce the number of point matches needed to compute a motion hypothesis. We then derive different algorithms based on this decoupling that allow an
efficient estimation. We also demonstrate how these algorithms can be used efficiently to compute an optimal inlier set using exhaustive search or histogram voting instead of a traditional RANSAC step. Our methods are evaluated on synthetic data
and on the KITTI data set, demonstrating that our methods are well suited for visual odometry in road driving scenarios.
在本文中，我们提出了基于单应性公式的双视图相对运动估计的最小解。
通过假定已知的垂直方向（例如，从IMU）并假设主导地平面，我们证明旋转和平移估计可以被解耦。该结果允许我们减少计算运动假设所需的点匹配的数量。然后，我们基于这种解耦导出不同的算法，从而实现有效的估计。我们还演示了如何使用穷举搜索或直方图投票而不是传统的RANSAC步骤来有效地使用这些算法来计算最佳内部集合。我们的方法在合成数据和KITTI数据集上进行评估，证明我们的方法非常适合于道路驾驶场景中的视觉测距。