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Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq
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Smartphone-Based Indoor Localization within a 13th Century Historic Building.

Toni Fetzer1, Frank Ebner2, Markus Bullmann3

  • 1Faculty of Computer Science and Business Information Systems, University of Applied Sciences Würzburg-Schweinfurt, 97074 Würzburg, Germany. toni.fetzer@fhws.de.

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Summary
This summary is machine-generated.

This study introduces an improved smartphone indoor localization system using a particle filter and novel navigation mesh. The system achieves accurate positioning even in challenging environments with a quick setup time.

Keywords:
PDRWi-Fiestimationhistoric buildingsindoor localizationnavigation meshparticle filtersample impoverishmentsensor fusionsmartphone

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Geographic Information Systems

Background:

  • Indoor localization systems are crucial for navigation in GPS-denied environments.
  • Existing methods like fingerprinting can be time-consuming and complex to set up.
  • Accurate and efficient indoor positioning remains a significant challenge.

Purpose of the Study:

  • To present an updated indoor localization system for smartphones.
  • To improve positioning accuracy and reduce setup time.
  • To address challenges like multimodal densities and sample impoverishment in particle filters.

Main Methods:

  • Utilized a particle filter for recursive state estimation with probabilistic sensor models.
  • Implemented an approximation scheme for kernel density estimation for exact position estimation.
  • Developed a novel state transition model based on navigation meshes and activity recognition (barometer, accelerometer).
  • Employed an optimization scheme with reference measurements for access point signal strength prediction.

Main Results:

  • Achieved small positioning errors even in challenging, historic building environments.
  • Demonstrated a setup time of less than 120 minutes for 2500 m² walkable area.
  • Successfully tackled issues like multimodal densities and sample impoverishment.
  • Validated the system through 28 measurement series across four test walks.

Conclusions:

  • The updated indoor localization system offers a fast-deployable solution with reasonable accuracy.
  • The novel navigation mesh and activity recognition enable continuous and smooth floor changes.
  • The system provides a fail-safe solution for indoor positioning in diverse conditions.