Matteo Frosi

Papers

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About

Matteo Frosi is a robotics researcher whose work focuses on advancing localization and mapping in challenging, GNSS-denied environments. His key research areas include Simultaneous Localization and Mapping (SLAM), sensor fusion, and loop closure detection, with a particular emphasis on leveraging non-traditional sensors and prior map information. Frosi’s major contributions center on enhancing the robustness and precision of autonomous navigation. His most cited work, "OSM-SLAM: Aiding SLAM with OpenStreetMaps priors" (8 citations), introduces a novel approach that integrates freely available cartographic data to improve SLAM performance, offering a cost-effective solution for real-world applications. He further investigates the accuracy of 6 DoF IMU-LiDAR localization in GPS-denied scenarios (6 citations), providing critical insights into sensor-based navigation for autonomous driving and harsh environment exploration. Most recently, his work on "RadarLCD" (3 citations) pioneers a learnable radar-based pipeline for loop closure detection, a fundamental task for correcting drift in long-term robot navigation. Through these contributions, Frosi is helping to build more reliable and autonomous systems for applications ranging from self-driving cars to subterranean exploration.