# Stereo relative pose from line and point feature triplets

## TL;DR

This paper introduces two new minimal solvers for stereo relative pose estimation using triplets of point or line features with known projections, improving motion estimation in stereo visual odometry.

## Contribution

The paper provides a complete classification of minimal cases with three features and introduces two solvers capable of handling all such cases, enhancing stereo visual odometry.

## Key findings

- New solvers improve motion estimation accuracy
- Complete classification of minimal cases
- Enhanced performance in visual SLAM systems

## Abstract

Stereo relative pose problem lies at the core of stereo visual odometry systems that are used in many applications. In this work, we present two minimal solvers for the stereo relative pose. We specifically consider the case when a minimal set consists of three point or line features and each of them has three known projections on two stereo cameras. We validate the importance of this formulation for practical purposes in our experiments with motion estimation. We then present a complete classification of minimal cases with three point or line correspondences each having three projections, and present two new solvers that can handle all such cases. We demonstrate a considerable effect from the integration of the new solvers into a visual SLAM system.