Simone Felicioni

About

Simone Felicioni is a robotics researcher whose work sits at the intersection of agricultural automation, micro aerial vehicle (MAV) control, and robust visual localization. His key contributions include the creation of the ARD‐VO dataset, a specialized real-world agricultural dataset for vineyards and olive groves that has already garnered 19 citations, addressing a critical gap in developing robotic solutions for farming. Felicioni has also advanced active tracking with D-VAT, an end-to-end visual active tracking system for MAVs (16 citations), which integrates vision and control for applications like surveillance and disaster recovery. His research further tackles ego-motion estimation through a benchmark analysis of data-driven and geometric approaches (5 citations), and he has explored vision-based topological localization as a more resilient alternative to metric pose estimation in challenging environments (3 citations). Additionally, his work on the Graph Object-based Localization Network (GOLN) pushes the boundaries of robotic localization beyond traditional SLAM limitations. With a portfolio that spans from practical agricultural datasets to cutting-edge MAV tracking and localization, Felicioni is making impactful strides in enabling more autonomous and adaptable robotic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   ARD‐VO: Agricultural robot data set of vineyards and olive groves

   19 citations · 2023
2. 2
   D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles

   16 citations · 2024
3. 3
   A benchmark analysis of data‐driven and geometric approaches for robot ego‐motion estimation

   5 citations · 2023
4. 4
   Vision-Based Topological Localization for MAVs

   3 citations · 2023
5. 5
   GOLN: Graph Object-based Localization Network

   2 citations · 2021

Key Collaborators

Contact & Links