Trifocal Tensor and Relative Pose Estimation with Known Vertical Direction
This work addresses pose estimation for applications like autonomous vehicles and drones, offering incremental improvements in efficiency and accuracy.
The paper tackles the problem of estimating relative camera poses among views with known vertical directions, presenting two solvers that require only four or three point correspondences, and shows superior accuracy on synthetic and KITTI data.
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.