Výsledky vyhledávání - "Cabrera, A. J."

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An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots

Autor: Cabrera, J. J., Céspedes, O. J., Cebollada, S., Reinoso, O., Payá, L.

This work presents an evaluation of CNN models and data augmentation to carry out the hierarchical localization of a mobile robot by using omnidireccional images. In this sense, an ablation study of different state-of-the-art CNN models used as backb

Externí odkaz: http://arxiv.org/abs/2407.10596

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An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments

Autor: Cabrera, J. J., Román, V., Gil, A., Reinoso, O., Payá, L.

Publikováno v: Cabrera, J.J., Rom\'an, V., Gil, A. , Reinoso, O. and Pay\'a, L. An experimental evaluation of Siamese Neural Networks for robot localization using omnidirectional imaging in indoor environments. Artificial Intellence Review 57, 198 (2024)

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 e

Externí odkaz: http://arxiv.org/abs/2407.10536

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TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation

Autor: Santo, Antonio, Cabrera, Juan J., Valiente, David, Viegas, Carlos, Gil, Arturo

This paper presents TE-NeXt, a novel and efficient architecture for Traversability Estimation (TE) from sparse LiDAR point clouds based on a residual convolution block. TE-NeXt block fuses notions of current trends such as attention mechanisms and 3D

Externí odkaz: http://arxiv.org/abs/2406.01395

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MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D Sparse Convolutions

Autor: Cabrera, J. J., Santo, A., Gil, A., Viegas, C., Payá, L.

This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by

Externí odkaz: http://arxiv.org/abs/2403.07593

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