# Monocular Visual Odometry with a Rolling Shutter Camera

## TL;DR

This paper introduces a novel monocular visual odometry algorithm tailored for rolling shutter cameras, effectively addressing distortion issues caused by rapid or abrupt camera motions, and validated on synthetic and real datasets.

## Contribution

The paper proposes a new RS essential matrix incorporating instantaneous velocities, improving ego-motion estimation accuracy for rolling shutter cameras under dynamic conditions.

## Key findings

- Outperforms previous methods in accuracy and robustness
- Effective in handling abrupt and fast camera motions
- Validated on synthetic and real datasets

## Abstract

Rolling Shutter (RS) cameras have become popularized because of low-cost imaging capability. However, the RS cameras suffer from undesirable artifacts when the camera or the subject is moving, or illumination condition changes. For that reason, Monocular Visual Odometry (MVO) with RS cameras produces inaccurate ego-motion estimates. Previous works solve this RS distortion problem with motion prediction from images and/or inertial sensors. However, the MVO still has trouble in handling the RS distortion when the camera motion changes abruptly (e.g. vibration of mobile cameras causes extremely fast motion instantaneously). To address the problem, we propose the novel MVO algorithm in consideration of the geometric characteristics of RS cameras. The key idea of the proposed algorithm is the new RS essential matrix which incorporates the instantaneous angular and linear velocities at each frame. Our algorithm produces accurate and robust ego-motion estimates in an online manner, and is applicable to various mobile applications with RS cameras. The superiority of the proposed algorithm is validated through quantitative and qualitative comparison on both synthetic and real dataset.