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A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications.

Tiago Davi Oliveira de Araújo1, Carlos Gustavo Resque Dos Santos2, Rodrigo Santos do Amor Divino Lima1

  • 1Computer Science Postgraduate Program, Federal University of Pará, Belém 66075-110, Brazil.

Sensors (Basel, Switzerland)
|October 3, 2019
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Summary

This study introduces an adaptive model for seamless transitions in Mobile Augmented Reality (MAR). The hybrid approach ensures fluid navigation across diverse environments by intelligently switching sensors and computer vision techniques.

Keywords:
adaptive modelindoormobile augmented realityoutdoor

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Robotics

Background:

  • Mobile Augmented Reality (MAR) systems struggle with seamless transitions between different environments, leading to user disorientation and reduced technology acceptance.
  • Current localization techniques are environment-specific; sensor-based methods face communication/infrastructure issues (GPS, Wi-Fi), while image-based methods are sensitive to lighting conditions.

Purpose of the Study:

  • To present an adaptive model for MAR applications that enables fluid transitions between diverse environments.
  • To mitigate issues in location, orientation, and registration during inter-environment transitions.

Main Methods:

  • A hybrid approach combining long-range sensors, short-range sensors, and computer vision techniques.
  • Development of a MAR application to test the adaptive model.
  • Navigation tests with volunteers transitioning between outdoor and indoor environments.

Main Results:

  • The adaptive model successfully facilitated seamless transitions between outdoor and indoor environments.
  • The MAR application self-adapted by dynamically switching sensors as needed.
  • Volunteer feedback indicated successful transitions with minimal disorientation.

Conclusions:

  • The proposed adaptive model effectively addresses the challenge of inter-environment transitions in MAR.
  • This hybrid approach enhances the robustness and user acceptance of MAR technology.
  • Future MAR applications can benefit from adaptive localization for improved navigation.