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Analysis of Data Augmentation Techniques for Mobile Robots Localization by Means of Convolutional Neural Networks


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Título :
Analysis of Data Augmentation Techniques for Mobile Robots Localization by Means of Convolutional Neural Networks
Autor :
Céspedes, Orlando Jose
Cebollada, Sergio
Cabrera, Juan José
Reinoso, Oscar  
Paya, Luis  
Editor :
Springer
Departamento:
Departamentos de la UMH::Ingeniería de Sistemas y Automática
Fecha de publicación:
2023-06
URI :
https://hdl.handle.net/11000/31595
Resumen :
This work presents an evaluation regarding the use of data augmentation to carry out the rough localization step within a hierarchical localization framework. The method consists of two steps: first, the robot captures an image and it is introduced into a CNN in order to estimate the room where it was captured (rough localization). After that, a holistic descriptor is obtained from the network and it is compared with the descriptors stored in the model. The most similar image provides the position where the robot captured the image (fine localization). Regarding the rough localization, it is essential that the CNN achieves a high accuracy, since an error in this step would imply a considerable localization error. With this aim, several visual effects were separately analyzed in order to know their impact on the CNN when data augmentation is tackled. The results permit designing a data augmentation which is useful for training a CNN that solves the localization problem in real operation conditions, including changes in the lighting conditions
Palabras clave/Materias:
Mobile Robotics
Omnidirectional Vision
Hierarchical Localization
Deep Learning
Data Augmentation
Área de conocimiento :
CDU: Ciencias aplicadas: Ingeniería. Tecnología
Tipo documento :
application/pdf
Derechos de acceso:
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Aparece en las colecciones:
Artículos Ingeniería de Sistemas y Automática



Creative Commons La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.