Please use this identifier to cite or link to this item:
https://hdl.handle.net/11000/32936
An experimental evaluation of Siamese Neural Networks
for robot localization using omnidirectional imaging
in indoor environments
Title: An experimental evaluation of Siamese Neural Networks
for robot localization using omnidirectional imaging
in indoor environments |
Authors: Cabrera, Juan José Román, Vicente Gil, Arturo Reinoso, Óscar Payá, Luis |
Editor: Springer |
Department: Departamentos de la UMH::Ingeniería de Sistemas y Automática |
Issue Date: 2024 |
URI: https://hdl.handle.net/11000/32936 |
Abstract:
The objective of this paper is to address the localization problem using omnidirectional
images captured by a catadioptric vision system mounted on the robot. For this purpose,
we explore the potential of Siamese Neural Networks for modeling indoor environments
using panoramic images as the unique source of information. Siamese Neural Networks
are characterized by their ability to generate a similarity function between two input data,
in this case, between two panoramic images. In this study, Siamese Neural Networks composed
of two Convolutional Neural Networks (CNNs) are used. The output of each CNN
is a descriptor which is used to characterize each image. The dissimilarity of the images is
computed by measuring the distance between these descriptors. This fact makes Siamese
Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate
an initial task strongly related to localization that consists in detecting whether two
images have been captured in the same or in different rooms. Next, we assess Siamese
Neural Networks in the context of a global localization problem. The results outperform
previous techniques for solving the localization task using the COLD-Freiburg dataset, in
a variety of lighting conditions, specially when using images captured in cloudy and night
conditions.
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Keywords/Subjects: Localization Omnidirectional imaging Holistic description Mobile robots Siamese Neural Network |
Knowledge area: CDU: Ciencias aplicadas: Ingeniería. Tecnología |
Type of document: application/pdf |
Access rights: info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI: https://doi.org/10.1007/s10462-024-10840-0 |
Appears in Collections: Artículos Ingeniería de Sistemas y Automática
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