Enhancing Indoor Smartphone Location Acquisition Using Floor Plans

  • Niranjini Rajagopal
  • , Patrick Lazik
  • , Nuno Pereira
  • , Sindhura Chayapathy
  • , Bruno Sinopoli
  • , Anthony Rowe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Indoor localization systems typically determine a position using either ranging measurements, inertial sensors, environmental-specific signatures or some combination of all of these methods. Given a floor plan, inertial and signature-based systems can converge on accurate locations by slowly pruning away inconsistent states as a user walks through the space. In contrast, range-based systems are capable of instantly acquiring locations, but they rely on densely deployed beacons and suffer from inaccurate range measurements given non-line-of-sight (NLOS) signals. In order to get the best of both worlds, we present an approach that systematically exploits the geometry information derived from building floor plans to directly improve location acquisition in range-based systems. Our solving approach can disambiguate multiple feasible locations taking into account a mix of LOS and NLOS hypotheses to accurately localize with significantly fewer beacons. We demonstrate our geometry-aware solving approach using a new ultrasonic beacon platform that is able to perform direct time-of-flight ranges on commodity smartphones. The platform uses Bluetooth Low Energy (BLE) for time synchronization and ultrasound for measuring propagation distance. We evaluate our system's accuracy with multiple deployments in a university campus and show that our approach shifts the 80% accuracy point from 4-8m to 1m as compared to solvers that do not use the floor plan information. We are able to detect and remove NLOS signals with 91.5% accuracy.

Original languageEnglish
Title of host publicationProceedings - 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages278-289
Number of pages12
ISBN (Electronic)9781538652985
DOIs
StatePublished - Oct 2 2018
Event17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018 - Porto, Portugal
Duration: Apr 11 2018Apr 13 2018

Publication series

NameProceedings - 17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018

Conference

Conference17th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2018
Country/TerritoryPortugal
CityPorto
Period04/11/1804/13/18

Keywords

  • floor plan integration
  • indoor localization
  • location acquisition
  • NLOC detection
  • range based localization
  • ray tracing

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