Papers
13
Total Citations
605
H-Index
8
About
Damien Vivet is a prominent researcher specializing in mobile robotics, autonomous navigation, and sensor fusion, with particular expertise in Simultaneous Localization and Mapping (SLAM) and perception systems for self-driving vehicles. His work spans a broad range of sensing modalities, from radar and LiDAR to camera-based systems, addressing core challenges in robust localization and mapping across diverse environments. Vivet's most influential contribution is his comprehensive 2020 review of Visual-LiDAR fusion-based SLAM, which has amassed an impressive 372 citations and has become a key reference for researchers in autonomous systems. Earlier in his career, he pioneered the use of rotating FMCW radar sensors for localization and mapping — a relatively unexplored area in mobile robotics — demonstrating their viability even at high speeds and in challenging atmospheric conditions, work that earned nearly 100 citations. His research portfolio also reflects a forward-looking embrace of deep learning, with notable contributions to LiDAR-camera auto-calibration methods such as NetCalib and sensor fusion pipelines for self-driving cars. More recently, his work on active SLAM through Fisher Information and traversability estimation highlights his continued innovation in 3D exploration for GNSS-denied environments. Vivet's research consistently bridges theoretical rigor with practical robotics applications.
Research Focus
Key Achievements
Top Papers
- 1A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping372 citations · 2020
- 2Localization and Mapping Using Only a Rotating FMCW Radar Sensor98 citations · 2013
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- 10Probabilistic Block-Matching based 6D camera localization4 citations · 2012