利用已知竖直方向，仅用4个点即可快速估算多视角相对位姿。
Trifocal Tensor and Relative Pose Estimation with Known Vertical Direction
- 基于已知竖直方向，仅需解两个旋转角和两个平移向量。
- 只需4个对应点即可求解，比传统方法更少，适合在RANSAC中使用。
- 适用于自动驾驶、无人机等含IMU设备的场景，精度优于现有方法。
本文提出两种新颖的求解器，用于在已知相机视角竖直方向的情况下估计多视图间的相对位姿。由于惯性测量单元（IMUs）广泛应用于自动驾驶汽车、手机和无人机，其提供的竖直方向信息易于获取。在此前提下，算法只需求解两个旋转角和两个平移向量。本文给出一种线性闭式解法，仅需三视图中四个点对应关系；同时提出一种最小解法，利用最新的Gröbner基求解器，仅需三个点对应关系。由于所需点数更少，该方法可高效嵌入RANSAC框架，用于剔除异常值并实现视觉里程计中的位姿估计。实验在合成数据及KITTI真实场景上进行，结果表明所提方法在位姿估计精度上优于其他对比方法。
原文摘要 · Abstract (English)
This work presents two novel solvers for estimating the relative poses among views with known vertical directions. The vertical directions of camera views can be easily obtained using inertial measurement units (IMUs) which have been widely used in autonomous vehicles, mobile phones, and unmanned aerial vehicles (UAVs). Given the known vertical directions, our lgorithms only need to solve for two rotation angles and two translation vectors. In this paper, a linear closed-form solution has been described, requiring only four point correspondences in three views. We also propose a minimal solution with three point correspondences using the latest Gröbner basis solver. Since the proposed methods require fewer point correspondences, they can be efficiently applied within the RANSAC framework for outliers removal and pose estimation in visual odometry. The proposed method has been tested on both synthetic data and real-world scenes from KITTI. The experimental results show that the accuracy of the estimated poses is superior to other alternative methods.
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