WiFi-SLAM Using Gaussian Process Latent Variable Models

Brian D. Ferris, Dieter Fox, Neil D. Lawrence
: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{pmlr-v-ferris-wifi07, title = {WiFi-SLAM Using Gaussian Process Latent Variable Models}, author = {Brian D. Ferris and Dieter Fox and Neil D. Lawrence}, pages = {2480--2485}, year = {}, editor = {}, 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 %C Proceedings of Machine Learning Research %D %E %F pmlr-v-ferris-wifi07 %I PMLR %J Proceedings of Machine Learning Research %P 2480--2485 %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-ferris-wifi07 PB - PMLR SP - 2480 DP - PMLR EP - 2485 L1 - 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.. (). WiFi-SLAM Using Gaussian Process Latent Variable Models. , in PMLR :2480-2485

Related Material