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Related Experiment Video

Updated: Jun 17, 2026

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
07:51

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Published on: March 14, 2017

PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones.

Yunqian Cheng1, Roberto Manduchi1

  • 1Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, United States.

International Conference on Indoor Positioning and Indoor Navigation : [Proceedings]. International Conference on Indoor Positioning and Indoor Navigation
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces PALMS, a smartphone indoor localization system using floor plans. It enhances accuracy and speed by using a novel particle filter initialization, improving mobile navigation.

Keywords:
CES constraintglobal localizationindoor localizationmobile smartphonesparticle filterplane detectionrelocalization

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Area of Science:

  • Robotics
  • Computer Science
  • Geographic Information Systems

Background:

  • Indoor localization is challenging due to GPS limitations.
  • Existing methods often require extensive environmental mapping or constant visual input.
  • Mobile smartphones are ubiquitous, offering potential for indoor navigation solutions.

Purpose of the Study:

  • To develop an innovative indoor global localization and relocalization system for smartphones.
  • To improve localization accuracy and reduce convergence time compared to traditional methods.
  • To create a scalable and practical indoor navigation solution without prior environmental fingerprinting.

Main Methods:

  • Utilizing publicly available floor plans for localization.
  • Adopting a dynamic localization approach combining instantaneous observation and odometry data.
  • Introducing a particle filter initialization method with the Certainly Empty Space (CES) constraint and principal orientation matching.

Main Results:

  • PALMS creates a spatial probability distribution for improved device location accuracy.
  • The system significantly reduces particle filter convergence time.
  • Experimental evaluations show PALMS outperforms traditional uniformly initialized particle filters.

Conclusions:

  • PALMS offers a more efficient and accessible approach to indoor wayfinding.
  • The system eliminates the need for prior environmental fingerprinting, enabling scalability.
  • This method provides a practical solution for smartphone-based indoor navigation.