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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/5/1174 |
| _version_ | 1811187562685923328 |
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| author | Xin Yuan José-Fernán Martínez-Ortega José Antonio Sánchez Fernández Martina Eckert |
| author_facet | Xin Yuan José-Fernán Martínez-Ortega José Antonio Sánchez Fernández Martina Eckert |
| author_sort | Xin Yuan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T14:04:24Z |
| format | Article |
| id | doaj.art-09fe46ee42ec436c84bb7a45017de354 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:04:24Z |
| publishDate | 2017-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-09fe46ee42ec436c84bb7a45017de3542022-12-22T04:19:55ZengMDPI AGSensors1424-82202017-05-01175117410.3390/s17051174s17051174AEKF-SLAM: A New Algorithm for Robotic Underwater NavigationXin Yuan0José-Fernán Martínez-Ortega1José Antonio Sánchez Fernández2Martina Eckert3Centro de Investigación en Tecnologías Software y Sistemas para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, SpainCentro de Investigación en Tecnologías Software y Sistemas para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, SpainCentro de Investigación en Tecnologías Software y Sistemas para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, SpainCentro de Investigación en Tecnologías Software y Sistemas para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, SpainIn 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.http://www.mdpi.com/1424-8220/17/5/1174underwater simultaneous localization and mapping (SLAM)augmented extended Kalman filter (AEKF)FastSLAM 2.0loop closurecomputational complexity |
| spellingShingle | Xin Yuan José-Fernán Martínez-Ortega José Antonio Sánchez Fernández Martina Eckert AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation Sensors underwater simultaneous localization and mapping (SLAM) augmented extended Kalman filter (AEKF) FastSLAM 2.0 loop closure computational complexity |
| 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 | underwater simultaneous localization and mapping (SLAM) augmented extended Kalman filter (AEKF) FastSLAM 2.0 loop closure computational complexity |
| url | http://www.mdpi.com/1424-8220/17/5/1174 |
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