Probabilistic Sensor Modeling
Probabilistic Sensor Modeling is a formal framework that characterizes sensor behavior through probability distributions to represent uncertainty in measurement processes, distinguishing itself from deterministic models by explicitly accounting for stochastic errors and environmental variability.
140
Publications
8K
Citations
364
Authors
189
Institutions
Publications per year
2017–2026
33
Parents
140
Using occupancy grids for mobile robot perception and navigation
Alberto Elfes · Computer · 1989 · 2.2K citations
Cleaning and querying noisy sensors
Eiman Elnahrawy, Badri Nath · 2003 · 183 citations
Multi-sensor fusion: a perspective
Jay K. Hackett, Mubarak Shah · 2002 · 149 citations · Full text
1–5 of 140
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National Institute of Astrophysics, Optics and Electronics, Mexico
3 publications · 83 citations
3
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2
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1–5 of 364
189
University of California, Berkeley
Berkeley, United States
5 h-index
7 publications
237 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
7 publications
2.5K citations
Instituto de Investigaciones Eléctricas
Cuernavaca, Mexico
3 h-index
3 publications
83 citations
University of Oxford
Oxford, United Kingdom
3 h-index
7 publications
736 citations

City University of Hong Kong
Hong Kong, Hong Kong
3 h-index
6 publications
96 citations
Tecnológico de Monterrey
Monterrey, Mexico
3 h-index
3 publications
83 citations
California Institute of Technology
Pasadena, United States
3 h-index
7 publications
2K citations

University of Freiburg
Freiburg im Breisgau, Germany
3 h-index
11 publications
600 citations
1–8 of 189
4 publications · 114 citations
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MSSP · 3 publications · 108 citations
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3 publications · 128 citations
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3 publications · 81 citations
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2 publications · 518 citations
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