Probabilistic Perception
Probabilistic Perception is a computational framework within multi-robot perception that models sensory interpretation and environmental state estimation under uncertainty using probability theory to represent and reason about ambiguity in sensor data, robotpose, and dynamic environments.
72
Publications
5.2K
Citations
199
Authors
105
Institutions
Publications per year
2017–2026
17
Parents
72
Uncertainty in perception and the Hierarchical Gaussian Filter
Christoph Mathys, Ekaterina I. Lomakina, Jean Daunizeau et al. · Frontiers in Human Neuroscience · 2014 · 512 citations · Full text
Probabilistic Algorithms in Robotics
Defense Technical Information Center (DTIC) · 2000 · 408 citations
1–5 of 72
199
4
h-index
4
h-index
3
h-index
2
h-index
Institut national de recherche en sciences et technologies du numérique, France
2 publications · 60 citations
2
h-index
1–5 of 199
105

Delft University of Technology
Delft, The Netherlands
4 h-index
11 publications
223 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
6 publications
618 citations
ETH Zurich
Zurich, Switzerland
2 h-index
6 publications
2.6K citations
Technical University of Munich
Munich, Germany
2 h-index
3 publications
50 citations
University of Zurich
Zurich, Switzerland
2 h-index
6 publications
2.6K citations

University of Freiburg
Freiburg im Breisgau, Germany
2 h-index
8 publications
1.7K citations
University College London
London, United Kingdom
2 h-index
4 publications
1.6K citations
University of Sheffield
Sheffield, United Kingdom
2 h-index
8 publications
276 citations
1–8 of 105
Repository · 4 publications · 315 citations
4
h-index
2 publications · 255 citations
2
h-index
IJRR · 2 publications · 558 citations
2
h-index
2 publications · 112 citations
2
h-index
2 publications · 401 citations
2
h-index
1–5