An exploration of location error estimation

  • Authors:
  • David Dearman;Alex Varshavsky;Eyal De Lara;Khai N. Truong

  • Affiliations:
  • Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada;Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

  • Venue:
  • UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
  • Year:
  • 2007

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Abstract

Many existing localization systems generate location predictions, but fail to report how accurate the predictions are. This paper explores the effect of revealing the error of location predictions to the end-user in a location finding field study. We report findings obtained under four different error visualization conditions and show significant benefit in revealing the error of location predictions to the user in location finding tasks. We report the observed influences of error on participants' strategies for location finding. Additionally, given the observed benefit of a dynamic estimate of error, we design practical algorithms for estimating the error of a location prediction. Analysis of the algorithms shows a median estimation inaccuracy of up to 50m from the predicted location's true error.