Monte Carlo Localization
Monte Carlo Localization is a probabilistic localization algorithm that belongs to the class of sampling-based methods, distinguished by its use of a particle filter to approximate a robot’s posterior probability distribution over its pose in an environment through repeated sampling and weighting based on sensor observations.
376
Publications
22.6K
Citations
972
Authors
359
Institutions
Publications per year
2017–2026
118
376
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
Localization for mobile sensor networks
Lingxuan Hu, David Evans · 2004 · 1.1K citations
Computing, Geoinformatics, Global Navigation Satellite Systems +14
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359

University of Freiburg
Freiburg im Breisgau, Germany
14 h-index
44 publications
5K citations
University of Bonn
Bonn, Germany
11 h-index
45 publications
3.8K citations

Universidad de Sevilla
Seville, Spain
7 h-index
10 publications
480 citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
18 publications
1.5K citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
16 publications
9.8K citations

Universidad Pablo de Olavide
Seville, Spain
5 h-index
8 publications
245 citations
Örebro University
Örebro, Sweden
5 h-index
16 publications
955 citations

University of Tübingen
Tübingen, Germany
5 h-index
14 publications
298 citations
1–8 of 359
15 publications · 540 citations
13
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7 publications · 255 citations
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6 publications · 752 citations
6
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5 publications · 130 citations
6
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5 publications · 96 citations
5
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