Trifocal Tensor and Relative Pose Estimation with Known Vertical Direction

TL;DR

This paper introduces two efficient solvers for estimating relative camera poses with known vertical directions, leveraging inertial data, and demonstrates their superior accuracy and efficiency in real-world and synthetic tests.

Contribution

The paper presents novel minimal and linear solvers for relative pose estimation using known vertical directions, reducing point correspondences needed and improving robustness.

Findings

Superior accuracy over existing methods

Efficient in RANSAC-based outlier removal

Validated on KITTI dataset and synthetic data

Abstract

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 Gr\"obner 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.