Abstract
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.
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Acknowledgements
This work has been supported by the ValgrAI (Valencian Graduate School and Research Network for Artificial Intelligence) and Generalitat Valenciana. This work is also part of the project PID2020-116418RB-I00 funded by MCIN/AEI/10.13039/501100011033, and of the project PROMETEO/2021/075 funded by Generalitat Valenciana.
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Céspedes, O.J., Cebollada, S., Cabrera, J.J., Reinoso, O., Payá, L. (2023). Analysis of Data Augmentation Techniques for Mobile Robots Localization by Means of Convolutional Neural Networks. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_42
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