B. Ferris, D. Fox, and N. Lawrence.
WiFi-SLAM Using Gaussian
Process Latent Variable Models
Proc. of the International Joint Conference on
Artificial Intelligence (IJCAI), 2007
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.
Full paper [pdf]
(372 kb, 6 pages)
[To the RSE-lab]