Davide Dorigoni

University of Trento, Fondazione Bruno Kessler

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About

Davide Dorigoni’s research lies at the intersection of robotics, autonomous navigation, and precision agriculture, with a core focus on improving the reliability and affordability of 3D mapping systems. His work addresses fundamental challenges in Simultaneous Localization and Mapping (SLAM), particularly the delayed SLAM problem, where he introduced an uncertainty-driven analysis that enhances how autonomous mobile robots interpret noisy sensor data from encoders and LIDAR. This contribution, published in 2021, has garnered early recognition with 4 citations, signaling its relevance to the robotics community. More recently, Dorigoni has advanced precision agriculture by developing a cost-effective 3D LiDAR deskewing method using an Extended Kalman Filter (EKF). This work, from 2024, tackles the critical need for accurate point cloud mapping in field planning, navigation, and crop monitoring—all while minimizing computational demands. By making robust 3D mapping accessible and affordable, Dorigoni’s research directly supports the growing trend of smart farming, where autonomous systems must operate reliably in dynamic, unstructured environments. His contributions demonstrate a clear trajectory from foundational SLAM theory to applied agricultural robotics, positioning him as a promising researcher bridging the gap between algorithmic innovation and real-world deployment.