Nico F. Declercq
Papers
4
Total Citations
29
H-Index
3
About
Nico F. Declercq is a leading researcher at the intersection of robotics, non-destructive testing, and ultrasonic sensing. His primary research focuses on developing autonomous robotic inspection systems for large metal structures, such as storage tanks and ship hulls, using ultrasonic guided waves. Declercq’s major contributions include pioneering FastSLAM approaches that integrate beamforming maps to simultaneously localize robots and map plate geometries, enabling precise navigation in GPS-denied environments. His work on learning propagation properties of rectangular metal plates has advanced Lamb wave-based mapping, while his combined grid and feature-based mapping frameworks offer robust spatial representations for industrial inspection. With over 29 citations across his most-cited papers, including a 2021 FastSLAM study (15 citations) and a 2022 learning-based mapping paper (8 citations), his research has significant impact on structural health monitoring. Notably, his Monte-Carlo localization method on metal plates demonstrates practical localization accuracy, pushing the boundaries of autonomous ultrasonic inspection. Declercq’s innovative integration of robotics and acoustics is shaping the future of safe, efficient infrastructure maintenance.
Research Focus
Key Achievements
Top Papers
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