1 citations · 1 across the 7 of their papers we have counts for
7 papers
Heterogeneous LiDAR Early Fusion and Learned Re-Ranking Strategy for Robust Long-Term Place Recognition in Unstructured Environments
Judith Vilella-Cantos, Juan José Cabrera, Mónica Ballesta +2
Robust localization in unstructured environments, such as agricultural fields, is a critical challenge for autonomous systems. LiDAR sensors provide detailed 3D information about t…
Advanced techniques and applications of LiDAR Place Recognition in Agricultural Environments: A Comprehensive Survey
Judith Vilella-Cantos, Mónica Ballesta, David Valiente +2
An optimal solution to the localization problem is essential for developing autonomous robotic systems. Apart from autonomous vehicles, precision agriculture is one of the elds tha…
Low Cost, High Efficiency: LiDAR Place Recognition in Vineyards with Matryoshka Representation Learning
Judith Vilella-Cantos, Mauro Martini, Marcello Chiaberge +2
Localization in agricultural environments is challenging due to their unstructured nature and lack of distinctive landmarks. Although agricultural settings have been studied in the…
A Coarse to Fine 3D LiDAR Localization with Deep Local Features for Long Term Robot Navigation in Large Environments
Míriam Máximo, Antonio Santo, Arturo Gil +2
The location of a robot is a key aspect in the field of mobile robotics. This problem is particularly complex when the initial pose of the robot is unknown. In order to find a solu…
MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity
Judith Vilella-Cantos, Juan José Cabrera, Luis Payá +2
In autonomous navigation systems, the solution of the place recognition problem is crucial for their safe functioning. But this is not a trivial solution, since it must be accurate…
TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation
Antonio Santo, Juan J. Cabrera, David Valiente +2
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 blo…
General Machine Learning Models for Interpreting and Predicting Efficiency Degradation in Organic Solar Cells
David Valiente, Fernando Rodríguez-Mas, Juan V. Alegre-Requena +3
This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar…