Monte Carlo Localization: Efficient Position Estimation for Mobile Robots

Dieter Fox, Wolfram Burgard, Frank Dellaert, and Sebastian Thrun
Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI'99)., July, 1999.


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Abstract
This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation ``where needed.'' The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.

Notes
Sponsor: NSF, DARPA via TACOM; Rome Labs; EC
Grant ID: DAAE07-98-C-L032; F30602-98-2-0137; ERBFMRX-CT96-0049

Text Reference
Dieter Fox, Wolfram Burgard, Frank Dellaert, and Sebastian Thrun, "Monte Carlo Localization: Efficient Position Estimation for Mobile Robots," Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI'99)., July, 1999.

BibTeX Reference
@inproceedings{Fox_1999_534,
   author = "Dieter Fox and Wolfram Burgard and Frank Dellaert and Sebastian Thrun",
   title = "Monte Carlo Localization: Efficient Position Estimation for Mobile Robots",
   booktitle = "Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI'99).",
   month = "July",
   year = "1999",
}