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AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Centro de Investigación en Tecnologías Software y Sistemas para la Sostenibilidad (CITSEM), Campus Sur, Universidad Politécnica de Madrid (UPM), Madrid 28031, Spain
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Sensors 2017, 17(5), 1174;
Received: 22 February 2017 / Revised: 8 May 2017 / Accepted: 19 May 2017 / Published: 21 May 2017
PDF [5969 KB, uploaded 21 May 2017]


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. View Full-Text
Keywords: underwater simultaneous localization and mapping (SLAM); augmented extended Kalman filter (AEKF); FastSLAM 2.0; loop closure; computational complexity underwater simultaneous localization and mapping (SLAM); augmented extended Kalman filter (AEKF); FastSLAM 2.0; loop closure; computational complexity

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Yuan, X.; Martínez-Ortega, J.-F.; Fernández, J.A.S.; Eckert, M. AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation. Sensors 2017, 17, 1174.

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