1 code implementation • 1 Mar 2023 • T. Barros, L. Garrote, P. Conde, M. J. Coombes, C. Liu, C. Premebida, U. J. Nunes
In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness.
no code implementations • 2 Dec 2021 • G. Melotti, W. Lu, P. Conde, D. Zhao, A. Asvadi, N. Gonçalves, C. Premebida
It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives.
1 code implementation • 2 Aug 2021 • T. Barros, P. Conde, G. Gonçalves, C. Premebida, M. Monteiro, C. S. S. Ferreira, U. J. Nunes
In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods.
no code implementations • 29 May 2020 • G. Melotti, C. Premebida, J. J. Bird, D. R. Faria, N. Gonçalves
Deep networks are currently the state-of-the-art for sensory perception in autonomous driving and robotics.
no code implementations • 17 Jun 2016 • C. Premebida, L. Garrote, A. Asvadi, A. Pedro Ribeiro, U. Nunes
High resolution depth-maps, obtained by upsampling sparse range data from a 3D-LIDAR, find applications in many fields ranging from sensory perception to semantic segmentation and object detection.