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.

Abstract

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},
  pages        = {(accepted)},
  month        = {Nov.},
  address      = {Macau, (IROS ICROS Best Application Paper Award.
		 Finalist)}
}

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Mon Sep 16 06:21:02 EDT 2019