WiFi-SLAM Using Gaussian Process Latent Variable Models

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 un-labeled signal strength data. We show how GP-LVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topo-logical 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.

Extracted Key Phrases

3 Figures and Tables

Showing 1-10 of 192 extracted citations
020406080'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

334 Citations

Semantic Scholar estimates that this publication has received between 262 and 428 citations based on the available data.

See our FAQ for additional information.