WiFi-SLAM Using Gaussian Process Latent Variable Models

Brian D. Ferris; Dieter Fox; Neil D. Lawrence

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WiFi-SLAM Using Gaussian Process Latent Variable Models

Brian D. Ferris, Dieter Fox, Neil D. Lawrence

Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007):2480-2485, 2007.

Abstract

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP-LVM) to determine the latent-space locations of unlabeled signal strength data. We show how GP-LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

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Cite this Paper

BibTeX


@InProceedings{Ferris:wifi07,
  title = 	 {{WiFi-SLAM} Using {G}aussian Process Latent Variable Models},
  author = 	 {Ferris, Brian D. and Fox, Dieter and Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007)},
  pages = 	 {2480--2485},
  year = 	 {2007},
  editor = 	 {Veloso, Manuela M.},
  pdf = 	 {http://www.ijcai.org/papers07/Papers/IJCAI07-399.pdf},
  url = 	 {/publications/ferris-wifi07.html},
  abstract = 	 {WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP-LVM) to determine the latent-space locations of unlabeled signal strength data. We show how GP-LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.}
}

Endnote

%0 Conference Paper
%T WiFi-SLAM Using Gaussian Process Latent Variable Models
%A Brian D. Ferris
%A Dieter Fox
%A Neil D. Lawrence
%B Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007)
%D 2007
%E Manuela M. Veloso	
%F Ferris:wifi07
%P 2480--2485
%U /publications/ferris-wifi07.html
%X WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP-LVM) to determine the latent-space locations of unlabeled signal strength data. We show how GP-LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.

RIS


TY  - CPAPER
TI  - WiFi-SLAM Using Gaussian Process Latent Variable Models
AU  - Brian D. Ferris
AU  - Dieter Fox
AU  - Neil D. Lawrence
BT  - Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007)
DA  - 2007/01/01
ED  - Manuela M. Veloso	
ID  - Ferris:wifi07
SP  - 2480
EP  - 2485
L1  - http://www.ijcai.org/papers07/Papers/IJCAI07-399.pdf
UR  - /publications/ferris-wifi07.html
AB  - WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GP-LVM) to determine the latent-space locations of unlabeled signal strength data. We show how GP-LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
ER  -

APA


Ferris, B.D., Fox, D. & Lawrence, N.D.. (2007). WiFi-SLAM Using Gaussian Process Latent Variable Models. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007):2480-2485 Available from /publications/ferris-wifi07.html.