WiFi-SLAM Using Gaussian Process Latent Variable Models

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 (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, 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.

[1]  Dieter Fox,et al.  Gaussian Processes for Signal Strength-Based Location Estimation , 2006, Robotics: Science and Systems.

[2]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[3]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[4]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[5]  Joaquin Quiñonero Candela,et al.  Local distance preservation in the GP-LVM through back constraints , 2006, ICML.

[6]  John J. Leonard,et al.  Pure range-only sub-sea SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[7]  Justin Murray,et al.  Volume 23 , 1988, Experimental Gerontology.

[8]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[9]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[10]  LawrenceNeil Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005 .

[11]  T. Wassmer 6 , 1900, EXILE.

[12]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[13]  Dieter Fox,et al.  Large-Scale Localization from Wireless Signal Strength , 2005, AAAI.

[14]  Anton Schwaighofer,et al.  GPPS: A Gaussian Process Positioning System for Cellular Networks , 2003, NIPS.

[15]  Michael H. Bowling,et al.  Subjective Localization with Action Respecting Embedding , 2005, ISRR.

[16]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[17]  Andreas Haeberlen,et al.  Practical robust localization over large-scale 802.11 wireless networks , 2004, MobiCom '04.