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AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation
In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470919/ https://www.ncbi.nlm.nih.gov/pubmed/28531135 http://dx.doi.org/10.3390/s17051174 |
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author | Yuan, Xin Martínez-Ortega, José-Fernán Fernández, José Antonio Sánchez Eckert, Martina |
author_facet | Yuan, Xin Martínez-Ortega, José-Fernán Fernández, José Antonio Sánchez Eckert, Martina |
author_sort | Yuan, Xin |
collection | PubMed |
description | In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure. |
format | Online Article Text |
id | pubmed-5470919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54709192017-06-16 AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation Yuan, Xin Martínez-Ortega, José-Fernán Fernández, José Antonio Sánchez Eckert, Martina Sensors (Basel) Article In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure. MDPI 2017-05-21 /pmc/articles/PMC5470919/ /pubmed/28531135 http://dx.doi.org/10.3390/s17051174 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yuan, Xin Martínez-Ortega, José-Fernán Fernández, José Antonio Sánchez Eckert, Martina AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title | AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title_full | AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title_fullStr | AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title_full_unstemmed | AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title_short | AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation |
title_sort | aekf-slam: a new algorithm for robotic underwater navigation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470919/ https://www.ncbi.nlm.nih.gov/pubmed/28531135 http://dx.doi.org/10.3390/s17051174 |
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