# Experimental Comparison of Open Source Visual-Inertial-Based State Estimation Algorithms in the Underwater Domain

## TL;DR

This paper evaluates ten open-source visual-inertial state estimation algorithms in underwater environments, highlighting their performance and challenges in marine conditions through extensive experiments.

## Contribution

It provides a comprehensive comparison of recent open-source algorithms in underwater settings, an area previously underexplored for such techniques.

## Key findings

- Performance varies significantly across algorithms in underwater conditions.
- Direct methods and tightly-coupled optimization techniques show different robustness levels.
- The datasets and evaluation framework are publicly available for future research.

## Abstract

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct methods and tightly-coupled optimization techniques that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.