Manuel Yguel

Université Grenoble Alpes, Institut national de recherche en sciences et technologies du numérique

Papers

Total Citations

135

H-Index

About

Manuel Yguel is a researcher whose work lies at the intersection of robotics, sensor fusion, and efficient spatial mapping. His key contributions center on developing compact and computationally efficient representations of occupancy grids—a fundamental tool for autonomous vehicles and robots to model their environment. Yguel pioneered the use of wavelet-based compression in mapping, introducing "Wavelet Occupancy Grids" to create compact yet accurate maps. He also advanced real-time performance by designing GPU-accelerated algorithms for constructing occupancy grids from multiple laser range-finders, enabling faster sensor fusion across varying resolutions. His work on the "Bayesian Occupation Filter" further refined probabilistic modeling of dynamic environments. With his most cited paper, "Update Policy of Dense Maps: Efficient Algorithms and Sparse Representation" (47 citations), Yguel demonstrated how to maintain dense maps with minimal computational overhead. Collectively, his papers have garnered over 135 citations, reflecting their impact on practical robotics and autonomous navigation. Yguel’s innovations in sparse representation and GPU-based processing continue to influence modern mapping systems, making his research essential reading for students and engineers working on real-time environmental perception.