Papers
158
Total Citations
3,618
H-Index
27
About
Emanuele Menegatti is a distinguished robotics researcher whose work spans autonomous navigation, human-robot interaction, sensor calibration, and people tracking. Based at the University of Padova, he has made lasting contributions to how robots perceive, localize themselves within, and interact with dynamic human environments. Menegatti's early foundational work focused on omnidirectional vision for robot navigation, pioneering image-based Monte Carlo localization and scan-matching techniques that enabled robots to operate reliably in cluttered, populated spaces (garnering over 100 citations each). He subsequently advanced the field of people detection and tracking, developing fast, robust RGB-D and LIDAR-based algorithms capable of monitoring individuals and groups in real time — work that has collectively attracted hundreds of citations and directly informs modern service robotics and crowd analysis. His 2014 IMU calibration method (243 citations) addressed a critical practical barrier in robotics by eliminating the need for costly external equipment, democratizing accessible sensor integration. His 2019 LIDAR-based behavior measurement system (405 citations) stands as his most impactful contribution, enabling long-term, wide-area human behavioral analysis essential for designing socially aware robots. Through the open-source OpenPTrack platform, Menegatti has also championed community-driven research, ensuring his innovations reach and benefit the broader robotics community worldwide.
Research Focus
Key Achievements
Top Papers
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- 4Fast RGB-D people tracking for service robots163 citations · 2014
- 5Tracking people within groups with RGB-D data142 citations · 2012
- 6Range-only SLAM with a mobile robot and a Wireless Sensor Networks131 citations · 2009
- 7Image-based Monte Carlo localisation with omnidirectional images112 citations · 2004
- 8General Hand–Eye Calibration Based on Reprojection Error Minimization102 citations · 2019
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