Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association (2004)by S. Thrun, M. Montemerlo, D. Koller, B. Wegbreit, J. Nieto, and E. Nebot
Abstract:
This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on real-world data.
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S. Thrun, M. Montemerlo, D. Koller, B. Wegbreit, J. Nieto, and E. Nebot (2004). "Fastslam: An efficient solution to the simultaneous localization and mapping problem with unknown data association." Journal of Machine Learning Research.
To appear.
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Bibtex citation
@article{Thrun+al:04b,
author = {S. Thrun and M. Montemerlo and D. Koller and B. Wegbreit and J. Nieto and E. Nebot},
title = {Fastslam: {A}n efficient solution to the simultaneous localization and mapping problem
with unknown data association},
journal = {Journal of Machine Learning Research},
note = {To appear},
year = 2004,
}
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