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Localization, Mapping and SLAM in Marine and Underwater Environments
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Localization, Mapping and SLAM in Marine and Underwater Environments

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 24924

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Antoni Burguera

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma, Illes Balears, Spain
Interests: robotics; localization; mapping; SLAM; underwater; sonar; computer vision; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Bonin-Font

E-Mail Website
Guest Editor
Departament de Matemàtiques i Informàtica, Universitat de les Illes Balears, Carretera de Valldemossa Km 7.5, 07122 Palma, Illes Balears, Spain
Interests: robot vision underwater; mobile robot navigation; localization of underwater robotics; visual simultaneous localization and mapping; convolutional neural networks; underwater inspection and intervention with robots; underwater robotic field applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of robots for field applications in underwater and marine environments is rapidly growing. Tasks such as shipwreck recovery, ocean exploration, biological sampling, and industrial infrastructure inspection, among others, can be achieved thanks to underwater and marine robots.

All these tasks have one common requirement: to properly model the environment and estimate the robots’ poses. Even though several mapping and SLAM methods exist, marine and underwater environments have some particularities that need to be taken into account, such as reduced vision range, water currents, communication problems, sonar inaccuracies, and unstructured environments.

The purpose of this Special Issue is to publish innovative research and application-oriented work related to underwater and marine localization and environment modelling.

Papers related (but not limited) to the following topics will be taken into consideration:

  • Marine and underwater localization and SLAM:
    • Visual;
    • Acoustic;
    • Laser based.
  • Marine and underwater exploration and mapping:
    • Map building;
    • 3D ocean floor reconstruction;
    • Mosaicking;
    • Object and scene recognition.
  • Marine and underwater multi-robot and multi-session localization, mapping, and SLAM.   
Prof. Dr. Antoni Burguera
Dr. Francisco Bonin-Font
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Underwater and marine robotics
  • Localization
  • Mapping
  • SLAM
  • Computer vision
  • Sonar
  • Scene recognition
  • Object recognition
  • Underwater inspection
  • Navigation

Published Papers (7 papers)

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16 pages, 10062 KiB  
Article
ROV Navigation in a Fish Cage with Laser-Camera Triangulation
by Magnus Bjerkeng, Trine Kirkhus, Walter Caharija, Jens T. Thielemann, Herman B. Amundsen, Sveinung Johan Ohrem and Esten Ingar Grøtli
J. Mar. Sci. Eng. 2021, 9(1), 79; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9010079 - 13 Jan 2021
Cited by 26 | Viewed by 3785
Abstract
Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D [...] Read more.
Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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14 pages, 11746 KiB  
Article
Underwater Pipe and Valve 3D Recognition Using Deep Learning Segmentation
by Miguel Martin-Abadal, Manuel Piñar-Molina, Antoni Martorell-Torres, Gabriel Oliver-Codina and Yolanda Gonzalez-Cid
J. Mar. Sci. Eng. 2021, 9(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse9010005 - 23 Dec 2020
Cited by 21 | Viewed by 3947
Abstract
During the past few decades, the need to intervene in underwater scenarios has grown due to the increasing necessity to perform tasks like underwater infrastructure inspection and maintenance or archaeology and geology exploration. In the last few years, the usage of Autonomous Underwater [...] Read more.
During the past few decades, the need to intervene in underwater scenarios has grown due to the increasing necessity to perform tasks like underwater infrastructure inspection and maintenance or archaeology and geology exploration. In the last few years, the usage of Autonomous Underwater Vehicles (AUVs) has eased the workload and risks of such interventions. To automate these tasks, the AUVs have to gather the information of their surroundings, interpret it and make decisions based on it. The two main perception modalities used at close range are laser and video. In this paper, we propose the usage of a deep neural network to recognise pipes and valves in multiple underwater scenarios, using 3D RGB point cloud information provided by a stereo camera. We generate a diverse and rich dataset for the network training and testing, assessing the effect of a broad selection of hyperparameters and values. Results show F1-scores of up to 97.2% for a test set containing images with similar characteristics to the training set and up to 89.3% for a secondary test set containing images taken at different environments and with distinct characteristics from the training set. This work demonstrates the validity and robust training of the PointNet neural in underwater scenarios and its applicability for AUV intervention tasks. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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31 pages, 4423 KiB  
Article
On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle
by Antoni Burguera and Francisco Bonin-Font
J. Mar. Sci. Eng. 2020, 8(8), 557; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8080557 - 24 Jul 2020
Cited by 10 | Viewed by 4182
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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25 pages, 10789 KiB  
Article
Towards Multi-Robot Visual Graph-SLAM for Autonomous Marine Vehicles
by Francisco Bonin-Font and Antoni Burguera
J. Mar. Sci. Eng. 2020, 8(6), 437; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8060437 - 14 Jun 2020
Cited by 11 | Viewed by 4328
Abstract
State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, [...] Read more.
State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, in a common reference system, several trajectories of different robots that participate simultaneously in a common mission. One of the main problems in centralized configurations, where the leader can receive multiple data from the rest of robots, is the limited communications bandwidth that delays the data transmission and can be overloaded quickly, restricting the reactive actions. This paper presents a new approach to Multi-robot visual graph Simultaneous Localization and Mapping (SLAM) that aims to perform a joined topological map, which evolves in different directions according to the different trajectories of the different robots. The main contributions of this new strategy are centered on: (a) reducing to hashes of small dimensions the visual data to be exchanged among all agents, diminishing, in consequence, the data delivery time, (b) running two different phases of SLAM, intra- and inter-session, with their respective loop-closing tasks, with a trajectory joining action in between, with high flexibility in their combination, (c) simplifying the complete SLAM process, in concept and implementation, and addressing it to correct the trajectory of several robots, initially and continuously estimated by means of a visual odometer, and (d) executing the process online, in order to assure a successful accomplishment of the mission, with the planned trajectories and at the planned points. Primary results included in this paper show a promising performance of the algorithm in visual datasets obtained in different points on the coast of the Balearic Islands, either by divers or by an Autonomous Underwater Vehicle (AUV) equipped with cameras. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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17 pages, 2474 KiB  
Article
An Improved Underwater Electric Field-Based Target Localization Combining Subspace Scanning Algorithm And Meta-EP PSO Algorithm
by Wenjing Shang, Wei Xue, Yingsong Li, Xiangshang Wu and Yidong Xu
J. Mar. Sci. Eng. 2020, 8(4), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8040232 - 26 Mar 2020
Cited by 9 | Viewed by 2112
Abstract
In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of [...] Read more.
In this paper, we propose an improved three-dimensional underwater electric field-based target localization method. This method combines the subspace scanning algorithm and the meta evolutionary programming (meta-EP) particle swarm optimization (PSO) algorithm. The subspace scanning algorithm is applied as the evaluation function of the electric field-based underwater target locating problem. The meta-EP PSO method is used to select M elite particles by the q-tournament selection method, which could effectively reduce the computational complexity of the three-dimensional underwater target localization. Moreover, the proposed meta-EP PSO optimization algorithm can avoid subspace scanning trapping into local minima. We also analyze the positioning performance of the uniform circular and cross-shaped electrodes arrays by using the subspace scanning algorithm combined with meta–EP PSO. According to the simulation, the calculation amount of the proposed algorithm is greatly reduced. Moreover, the positioning accuracy is effectively improved without changing the positioning accuracy and search speed. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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14 pages, 2282 KiB  
Article
An Approach for Diver Passive Detection Based on the Established Model of Breathing Sound Emission
by Qiang Tu, Fei Yuan, Weidi Yang and En Cheng
J. Mar. Sci. Eng. 2020, 8(1), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8010044 - 15 Jan 2020
Cited by 7 | Viewed by 3871
Abstract
Diver breathing sounds can be used as a characteristic for the passive detection of divers. This work introduces an approach for detecting the presence of a diver based on diver breathing sounds signals. An underwater channel model for passive diver detection is built [...] Read more.
Diver breathing sounds can be used as a characteristic for the passive detection of divers. This work introduces an approach for detecting the presence of a diver based on diver breathing sounds signals. An underwater channel model for passive diver detection is built to evaluate the impacts of acoustic energy transmission loss and ambient noise interference. The noise components of the observed signals are suppressed by spectral subtraction based on block-based threshold theory and smooth minimal statistic noise tracking theory. Then the envelope spectrum features of the denoised signal are extracted for diver detection. The performance of the proposed detection method is demonstrated through experimental analysis and numerical modeling. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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13 pages, 1185 KiB  
Letter
An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors
by Zhuang Xie, Jiahua Zhu, Chongyi Fan and Xiaotao Huang
J. Mar. Sci. Eng. 2020, 8(10), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/jmse8100757 - 28 Sep 2020
Cited by 3 | Viewed by 1671
Abstract
In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample [...] Read more.
In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample covariance matrix (DSCM), in which the auxiliary sensors are selected to decouple the array. The decoupled interference-plus-noise covariance matrix (DINCM) is reconstructed with the estimated interference SV and maximum eigenvalue of the DSCM. Furthermore, the desired signal SV is estimated as the corresponding eigenvector determined by the correlation coefficients of the assumed SV and eigenvectors. Finally, the optimal weighting vector is obtained by combining the reconstructed DINCM and the estimated desired signal SV. Our simulation results show significant signal-to-interference-plus-noise ratio (SINR) enhancement of the proposed method over existing methods under multiple types of array errors. Full article
(This article belongs to the Special Issue Localization, Mapping and SLAM in Marine and Underwater Environments)
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