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.

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 = {http://inverseprobability.com/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 http://inverseprobability.com/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 - http://inverseprobability.com/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 http://inverseprobability.com/publications/ferris-wifi07.html.

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