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Grant support

The research leading to the presented results has been undertaken within the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant Agreement n. 662107-SWARMs-ECSEL-2014-1, partially supported by the ECSEL JU and the Spanish Ministry of Economy and Competitiveness (Ref.: PCIN-2014-022-C02-02), and also by the China Scholarship Council (CSC), which has partially supported the first author's research described in this paper.

Analysis of institutional authors

Yuan, XinCorresponding AuthorMartínez-Ortega, José-FernánAuthorSanchez Fernandez, Jose AntonioAuthorEckert, MartinaAuthor

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June 9, 2019
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AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

Publicated to:Sensors. 17 (5): E1174- - 2017-05-01 17(5), DOI: 10.3390/s17051174

Authors: Yuan, Xin; Martinez-Ortega, Jose-Fernan; Sanchez Fernandez, Jose Antonio; Eckert, Martina

Affiliations

Grupo de Redes y Servicios de Próxima Generación (GRyS). Universidad Politécnica de Madrid - Author
Univ Politecn Madrid, Ctr Invest Tecnol Software & Sistemas Sostenibili, Campus Sur, Madrid 28031, Spain - Author

Abstract

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.

Keywords

augmented extended kalman filter (aekf)computational complexityfastslam 2.0loop closureAugmented extended kalman filter (aekf)Computational complexityEnvironmentsFastslam 2.0FilterLoop closureSimultaneous localizationUnderwater simultaneous localization and mapping (slam)

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2017, it was in position 16/61, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Instruments & Instrumentation. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Electrical and Electronic Engineering.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.11. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 1.06 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 8.84 (source consulted: Dimensions Sep 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-09-21, the following number of citations:

  • WoS: 30
  • Scopus: 36
  • Europe PMC: 5
  • Google Scholar: 48

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-09-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 66.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 66 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.5.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (YUAN, XIN) and Last Author (ECKERT, MARTINA).

the author responsible for correspondence tasks has been YUAN, XIN.