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

Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

Bharat Joshi , Sharmin Rahman, Michail Kalaitzakis, Brennan Cain, James Johnson, Marios Xanthidis, Nare Karapetyan, Alan Hernandez, Alberto Quattrini Li, Nikolaos Vitzilaios, Ioannis Rekleitis. Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems . IROS 2019 . Paper Bibtex

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.