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Towards Multi-Robot Visual Graph-SLAM for Autonomous Marine Vehicles
Open AccessFeature PaperArticle

On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle

Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Carretera de Valldemossa Km. 7.5, 07122 Palma, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Mar. Sci. Eng. 2020, 8(8), 557; https://doi.org/10.3390/jmse8080557
Received: 8 July 2020 / Revised: 21 July 2020 / Accepted: 22 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
This paper proposes a method to perform on-line multi-class segmentation of Side-Scan Sonar acoustic images, thus being able to build a semantic map of the sea bottom usable to search loop candidates in a SLAM context. The proposal follows three main steps. First, the sonar data is pre-processed by means of acoustics based models. Second, the data is segmented thanks to a lightweight Convolutional Neural Network which is fed with acoustic swaths gathered within a temporal window. Third, the segmented swaths are fused into a consistent segmented image. The experiments, performed with real data gathered in coastal areas of Mallorca (Spain), explore all the possible configurations and show the validity of our proposal both in terms of segmentation quality, with per-class precisions and recalls surpassing the 90%, and in terms of computational speed, requiring less than a 7% of CPU time on a standard laptop computer. The fully documented source code, and some trained models and datasets are provided as part of this study. View Full-Text
Keywords: sonar; underwater robotics; acoustic image segmentation; neural network sonar; underwater robotics; acoustic image segmentation; neural network
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Burguera, A.; Bonin-Font, F. On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle. J. Mar. Sci. Eng. 2020, 8, 557.

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