Francisco A. Candelas — accepted AI-conference papers

2024

Geo-Localization Based on Dynamically Weighted Factor-Graph

Miguel Ángel Muñoz-Bañón, Alejandro Olivas, Edison Velasco-Sánchez, Francisco A. Candelas, Fernando Torres

RA-L 2024

Feature-based geo-localization relies on associating features extracted from aerial imagery with those detected by the vehicle's sensors. This requires that the type of landmarks must be observable from both sources. This lack of variety of feature types generates poor representations that lead to o

Cited by 7 Source ↗Scholar ↗

2024

Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

Edison Velasco-Sánchez, Luis F. Recalde, Bryan S. Guevara, José Varela Aldás, Francisco A. Candelas, Santiago T. Puente +1

RA-L 2024

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on the capture and detai

Cited by 10 Source ↗code ↗Scholar ↗

2022

Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings

Miguel Ángel Muñoz-Bañón, Jan-Hendrik Pauls, Haohao Hu, Christoph Stiller, Francisco A. Candelas, Fernando Torres

RA-L 2022

Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data. However, the landmarks that can be observed from both aerial imagery and on-board sensors is limited. This leads to ambiguities or alia

Cited by 7 Source ↗Scholar ↗