An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
Sharmin Rahman, Alberto {Quattrini Li}, Ioannis Rekleitis
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019.
[94 citations]Abstract
This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization – one of the main problems affecting other packages in underwater domain – by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.
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BibTeX
@inproceedings{RahmanIROS2019a,
author = {Sharmin Rahman and Alberto {Quattrini Li} and Ioannis
Rekleitis},
title = {{An Underwater SLAM System using Sonar, Visual, Inertial,
and Depth Sensor}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS)},
year = {2019},
doi = {10.1109/iros40897.2019.8967703},
pages = {1861--1868},
month = {Nov.},
address = {Macau, (IROS ICROS Best Application Paper Award.
Finalist)}
}
Thu Apr 18 06:21:02 EDT 2024