Emmanuel Piat
Papers
3
Total Citations
7
H-Index
2
About
Emmanuel Piat is a robotics and autonomous systems researcher whose work centers on probabilistic modeling, sensor fusion, and environment representation for mobile robots. His research contributions, developed in the early 2000s, addressed fundamental challenges in enabling autonomous robots to navigate and map complex, previously unknown environments. Piat's most notable work explores hierarchical path planning in cross-country environments, where robots must reason about uncertain terrain using 3D data structured into probabilistic polygonal hierarchies. This approach provided a principled framework for handling environmental complexity in unstructured outdoor settings. Complementing this, his research on probabilistic belief fusion introduced systematic methodologies for merging sensor-derived degrees of confidence — particularly from range data — to construct reliable occupancy grids for indoor mobile robot navigation. His contributions sit at the intersection of probabilistic reasoning and spatial representation, areas that have since become foundational to modern robotics and autonomous vehicle research. By formalizing how conflicting or uncertain sensory beliefs can be coherently merged, Piat helped lay groundwork for more robust robot perception systems. While his citation counts remain modest, his work reflects early, technically rigorous contributions to a field that has grown substantially in scope and importance in subsequent decades.
Research Focus
Key Achievements
Top Papers
- 1Hierarchical path planning on probabilistically labeled polygons3 citations · 2002
- 2
- 3