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Abstract: Monte Carlo localization (MCL) is a Bayesian algorithm for mobile robot localization based on particle filters, which has enjoyed great practical success. This paper points out a limitation of MCL which is counter-intuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of a new proposal distribution for the Monte Carlo sampling step. Extensive experimental results with physical robots suggest that the new algorithm is... (Update)
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BibTeX entry: (Update)
S. Thrun, D. Fox, and W. Burgard. Monte carlo localization with mixture proposal distribution. In Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX, 2000. AAAI. http://citeseer.ist.psu.edu/article/thrun00monte.html More
@inproceedings{ thrun00monte,
author = "Sebastian Thrun and Dieter Fox and Wolfram Burgard",
title = "Monte Carlo Localization with Mixture Proposal Distribution",
booktitle = "{AAAI}/{IAAI}",
pages = "859-865",
year = "2000",
url = "citeseer.ist.psu.edu/article/thrun00monte.html" }
Citations (may not include all citations):364 Condensation: conditional density propagation for visual tra.. - Isard, Blake
362 An introduction to hidden markov models (context) - Rabiner, Juang - 1986
137 Filtering via simulation: auxiliary particle filter - Pitt, Shephard - 1999
126 Sequential monte carlo methods for dynamic systems - Liu, Chen - 1998
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85 On sequential simulation-based methods for Bayesian filterin.. - Doucet - 1998
82 Monte carlo localization: Efficient position estimation for .. - Fox, Burgard et al.
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48 Efficient Memory-based Learning for Robot Control - Moore - 1990
47 The Kalman filter: An introduction to concepts (context) - Maybeck - 1990
47 MINERVA: A second generation mobile tourguide robot (context) - Thrun, Bennewitz et al.
40 Sensor resetting localization for poorly modelled mobile rob.. - Lenser, Veloso - 2000
36 Error correction in mobile robot map learning (context) - Engelson, McDermott
23 Backward simulation in bayesian networks (context) - Fung, Favero
9 Combining computer graphics and computer vision for probabil.. - Denzler, Heigl et al. - 1999
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