Papers
29
Total Citations
540
H-Index
13
About
Thierry Peynot is a prominent robotics researcher whose work spans field robotics, autonomous perception, and medical imaging, with a particular focus on making robotic systems reliable in challenging real-world environments. His research has made significant contributions to terrain traversability analysis for autonomous ground vehicles, a survey on which has already accumulated 83 citations since 2022, establishing it as a key reference in the field. Peynot has pioneered work in multi-sensor fusion and calibration, developing methods to identify and resolve discrepancies between heterogeneous sensors such as cameras, LiDAR, and radar, ensuring robust perception in adverse conditions including smoke, fire, and nighttime environments. His 2013 paper on selective visual-thermal imaging fusion for resilient localization earned 61 citations, reflecting its practical significance for long-term autonomous missions. Notably, Peynot has extended his expertise into medical robotics, leading development of the ArthroSLAM and Dense-ArthroSLAM systems, which apply simultaneous localization and mapping techniques to minimally invasive arthroscopic surgery. His interdisciplinary reach — from dusty outback terrain to human joints — demonstrates an exceptional ability to translate core robotics perception principles into transformative applications across domains.
Research Focus
Key Achievements
Top Papers
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- 5Image-Based Visual Servoing With Light Field Cameras35 citations · 2017
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- 7Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning23 citations · 2021
- 8Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces22 citations · 2014
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- 10Laser-camera data discrepancies and reliable perception in outdoor robotics20 citations · 2010