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Feature extraction for underwater visual SLAM

Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency

This work was partially funded through the Spanish Ministry of Education and Science (MCINN) under grant CTM2010-15216 and the EU under grant FP7-ICT-2009-248497

© Oceans (2011: Santander: Espanya). OCEANS, 2011 IEEE - Spain

Institute of Electrical and Electronics Engineers (IEEE)

Author: Aulinas Masó, Josep M.
Carreras Pérez, Marc
Lladó Bardera, Xavier
Salvi, Joaquim
García Campos, Rafael
Prados Gutiérrez, Ricard
Date: 2011 June
Abstract: Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency
This work was partially funded through the Spanish Ministry of Education and Science (MCINN) under grant CTM2010-15216 and the EU under grant FP7-ICT-2009-248497
Format: application/pdf
ISBN: 978-1-4577-0088-0
Document access: http://hdl.handle.net/10256/13696
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: MICINN/PN 2011-2014/CTM2010-15216
Versió postprint del document publicat a: http://dx.doi.org/10.1109/Oceans-Spain.2011.6003474
Articles publicats (D-ATC)
info:eu-repo/grantAgreement/EC/FP7/248497
Is part of: © Oceans (2011: Santander: Espanya). OCEANS, 2011 IEEE - Spain
Rights: Tots els drets reservats
Subject: Robots mòbils
Mobile robots
Vehicles submergibles
Submersibles
Imatges -- Segmentació
Image processing
Title: Feature extraction for underwater visual SLAM
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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