Flavio Fontana
Papers
6
Total Citations
1,214
H-Index
5
About
Flavio Fontana is a leading researcher in field robotics and autonomous navigation, with a focus on enabling robots—particularly micro aerial vehicles (MAVs)—to operate in unstructured, GPS-denied environments. His work bridges computer vision, machine learning, and control systems. Fontana’s most cited paper (694 citations) introduced a machine learning approach for visual perception of forest trails from a single monocular image, a breakthrough that allows ground robots to autonomously navigate natural terrain without relying on high-level features. He also made seminal contributions to vision-based autonomous flight, demonstrating live dense 3D mapping with a quadrotor MAV (240 citations) and developing robust methods for automatic re-initialization and failure recovery during aggressive flight (108 citations). His research on continuous on-board monocular-vision-based elevation mapping (100 citations) enabled real-time terrain reconstruction and autonomous landing using only a smartphone processor. Additionally, Fontana pioneered aerial-ground robot collaboration in disaster scenarios, where an aerial robot maps an area and guides a ground robot through movable obstacles. His work has profoundly impacted search-and-rescue, disaster response, and field robotics, setting new standards for autonomous operation in challenging environments.
Research Focus
Key Achievements
Top Papers
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- 5Aerial-guided navigation of a ground robot among movable obstacles70 citations · 2014
- 6Control of a swinging juggling robot2 citations · 2013