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Indoor Positioning on Smartphones Using Built-In Sensors and Visual Images.

Jiaqiang Yang1, Danyang Qin1,2, Huapeng Tang1

  • 1Department of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, China.

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Summary
This summary is machine-generated.

This study introduces a new visual localization method using smartphone sensors to estimate pedestrian pose. This approach enhances indoor navigation accuracy, particularly in complex environments like malls.

Keywords:
indoor localizationmachine learningsensorsvisual positioning

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Area of Science:

  • Computer Science
  • Robotics
  • Mobile Computing

Background:

  • Indoor localization is crucial for navigation but faces challenges like signal interference.
  • Visual localization offers high accuracy but is sensitive to user pose.
  • Existing methods struggle with pose variations in complex indoor environments.

Purpose of the Study:

  • To develop a robust indoor visual localization method.
  • To mitigate the impact of pedestrian photographing pose on localization accuracy.
  • To leverage smartphone sensors for improved indoor positioning.

Main Methods:

  • A machine learning classifier was developed using multiple smart sensors for pedestrian pose estimation.
  • The method integrates visual image localization with sensor-based pose estimation.
  • Smartphone's built-in sensors were utilized for pose data acquisition.

Main Results:

  • The proposed method significantly improves retrieval efficiency and localization accuracy.
  • Experimental results demonstrate good localization accuracy and robustness.
  • The approach was validated in common indoor scenes using standard smartphones.

Conclusions:

  • The multi-sensor-assisted visual localization method effectively addresses pose-related inaccuracies.
  • This technique offers a feasible and accurate solution for indoor visual information-based positioning.
  • The method shows potential for widespread application in future indoor navigation systems, including complex scenarios.