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Descripción
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| In this paper, a new method to improve the accuracy of underwater landmark maps is proposed. It employs an Augmented Extended Kalman Filter based Simultaneous Localization and Mapping algorithm, called AEKF-SLAM. The proposed AEKF-based SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stages (as in classical EKF), includes a newly proposed augmentation stage. The AEKF-SLAM simulation experiments, performed for underwater dense loop mapping and line mapping, show a very good performance in map management in terms of landmark addition and removal avoiding the long-term accumulation of clutter in the map. AEKF estimates the robot pose and seabed landmark positions accurately. Altogether, the proposed AEKF-SLAM algorithm achieves reliable detection of cycles in the map and consistent map update on loop closure. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Autonomous Robots |
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ISSN
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0929-5593 |
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Factor de impacto JCR
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2,244 |
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Información de impacto
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Datos JCR del año 2017 |
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Volumen
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DOI
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Número de revista
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Desde la página
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1 |
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Hasta la página
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16 |
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Mes
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SIN MES |
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Ranking
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JCR Q2 |