---
title: "WiFi-SLAM Using Gaussian Process Latent Variable Models"
authors:
  - "Brian Ferris"
  - "Dieter Fox"
  - "Neil D. Lawrence"
doi: ""
url: "https://ijcai.org/proceedings/2007/399"
pdf: "https://ijcai.org/proceedings/2007/0399.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2480-2484"
track: ""
keywords:
---

# WiFi-SLAM Using Gaussian Process Latent Variable Models

Brian Ferris, Dieter Fox, Neil D. Lawrence

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2480-2484.

## 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.