Alberto Dionigi
Papers
8
Total Citations
88
H-Index
5
About
Alberto Dionigi is a robotics researcher whose work sits at the intersection of visual perception, autonomous navigation, and active control, with a particular focus on aerial and agricultural robotics. His most significant contributions center on developing end-to-end active visual tracking systems for micro aerial vehicles, notably through his E-VAT and D-VAT frameworks, which have collectively garnered over 45 citations. These works advance beyond passive tracking by integrating vision and control to enable drones to actively pursue and maintain visual contact with moving targets—a capability critical for applications in surveillance, disaster response, and human assistance. Dionigi has also made notable contributions to visual simultaneous localization and mapping (SLAM) and ego-motion estimation, including benchmark studies that compare data-driven and geometric approaches for robot localization, as well as evaluations of SLAM performance in low-light environments. His work on the ARD‐VO dataset, which provides real-world agricultural data from vineyards and olive groves, addresses a critical gap in field robotics. Additionally, his research explores zero-shot sim-to-real transfer of reinforcement learning policies for quadrotor control and convex programming for optimal motion planning, demonstrating a broad technical range from theoretical optimization to practical deployment.
Research Focus
Key Achievements
Top Papers
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- 2ARD‐VO: Agricultural robot data set of vineyards and olive groves19 citations · 2023
- 3D-VAT: End-to-End Visual Active Tracking for Micro Aerial Vehicles16 citations · 2024
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