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Ministerio de Ciencia e Innovación (Espanya) | |
Aulinas Masó, Josep M.
Carreras Pérez, Marc Lladó Bardera, Xavier Salvi, Joaquim GarcÃa Campos, Rafael Prados Gutiérrez, Ricard |
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2018 June 5 | |
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 |
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http://hdl.handle.net/2072/319449 | |
eng | |
Institute of Electrical and Electronics Engineers (IEEE) | |
Tots els drets reservats | |
Robots mòbils
Mobile robots Vehicles submergibles Submersibles Imatges -- Segmentació Image processing |
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Feature extraction for underwater visual SLAM | |
info:eu-repo/semantics/article | |
Recercat |