Probabilistic Robot Localization
Probabilistic Robot Localization is a computational framework within robot localization that represents the robot’s belief about its position as a probability distribution over possible states, distinguishing itself from deterministic methods by explicitly accounting for uncertainty in both sensor measurements and motion actions through recursive Bayesian estimation.
215
Publications
19.4K
Citations
541
Authors
192
Institutions
Publications per year
2017–2026
36
Variants
Probabilistic Localization
Parents
215
Robust Monte Carlo localization for mobile robots
Sebastian Thrun, Dieter Fox, Wolfram Burgard et al. · Artificial Intelligence · 2001 · 1.8K citations
Monte Carlo localization for mobile robots
Frank Dellaert, D. Fox, Wolfram Burgard et al. · 2003 · 1.6K citations · Full text
Monte Carlo localization: efficient position estimation for mobile robots
Dieter Fox, Wolfram Burgard, Frank Dellaert et al. · 1999 · 1.1K citations
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192
Carnegie Mellon University
Pittsburgh, United States
17 h-index
38 publications
15.9K citations

University of Freiburg
Freiburg im Breisgau, Germany
13 h-index
41 publications
4.5K citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
13 publications
497 citations
Stanford University
Stanford, United States
7 h-index
13 publications
899 citations
Universidad de Zaragoza
Zaragoza, Spain
6 h-index
13 publications
1.5K citations

Universidad de Málaga
Málaga, Spain
6 h-index
25 publications
1.3K citations

The University of Sydney
Sydney, Australia
6 h-index
14 publications
3.2K citations

Universidad Pablo de Olavide
Seville, Spain
5 h-index
7 publications
257 citations
1–8 of 192
8 publications · 651 citations
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7 publications · 802 citations
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7 publications · 284 citations
7
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IJRR · 6 publications · 338 citations
6
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4 publications · 67 citations
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