Daniele De Martini
University of Oxford, Oxford Research Group, Science Oxford, University of Pavia, Politecnico di Torino
Papers
18
Total Citations
174
H-Index
6
About
Daniele De Martini is a robotics and autonomous systems researcher whose work spans robot localisation, radar-based perception, and multi-modal sensing for long-term outdoor autonomy. His research addresses some of the most challenging problems in mobile robotics: enabling robots to navigate reliably in adverse weather conditions and complex environments where conventional sensors falter. Among his most influential contributions is his pioneering work on cross-modal localisation, demonstrating how ground-based lidar systems can be localised using only publicly available overhead imagery — eliminating the need for costly prior sensor maps. This line of work has accumulated over 30 citations across multiple publications. His radar odometry research, leveraging constant-curvature motion constraints for non-holonomic robots, and his contribution to the Oxford Offroad Radar Dataset (OORD) have further strengthened the field's understanding of millimetre-wave radar for autonomous vehicles in challenging off-road conditions. De Martini has also engaged with emerging paradigms, including metaverse synchronisation frameworks (61 citations), sound-based localisation, and permissible route detection using radar with weak supervision. His development of weather-proof robotic platforms underscores a commitment to translating research into real-world deployment. Collectively, his work reflects a broad, practically grounded vision for robust, long-term robot autonomy.
Research Focus
Key Achievements
Top Papers
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- 4<i>OORD</i>: The Oxford Offroad Radar Dataset13 citations · 2024
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- 10Design of a Mobile Robot for Air Ducts Exploration4 citations · 2017