Marco Leonardi
Papers
2
Total Citations
10
H-Index
2
About
Marco Leonardi is a robotics researcher specializing in autonomous navigation and perception for underwater vehicles. His work addresses the critical challenge of enabling Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) to operate reliably in complex, unstructured environments such as nature protection areas, historical sites, and man-made underwater structures. A key contribution is his development of vision-based obstacle avoidance and motion tracking systems, which allow these vehicles to perform precise localization and maneuvering without relying on expensive or impractical external sensors. His 2017 paper on this topic has garnered 6 citations, establishing a foundation for practical underwater autonomy. Leonardi has also advanced simultaneous localization and mapping (SLAM) by introducing a convolutional autoencoder approach for robust loop closure detection. This 2018 work, with 4 citations, addresses the critical problem of correctly identifying when a robot has returned to a previously visited location—a failure that can destabilize entire navigation systems. By combining deep learning with classical robotics, Leonardi’s research pushes toward more resilient, self-sufficient underwater platforms capable of long-duration missions in sensitive or hazardous environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2