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Indoor Pedestrian Positioning Method Based on Ultra-Wideband with a Graph Convolutional Network and Visual Fusion.

Huizhen Mu1, Chao Yu1,2,3, Shuna Jiang1

  • 1Engineering Center of SHMEC for Space Information and GNSS, East China Normal University, Shanghai 200241, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel indoor positioning method combining ultra-wideband (UWB) and visual positioning. The integrated approach significantly enhances positioning accuracy for pedestrians, outperforming existing single-technology methods.

Keywords:
GCNUWBindoor fusion positioningparticle filtervision sensor

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Area of Science:

  • Robotics
  • Computer Vision
  • Signal Processing

Background:

  • Indoor positioning systems face challenges like signal interference and visual distortions, limiting accuracy.
  • Existing methods, such as ultra-wideband (UWB) and visual positioning, have limitations in precision and continuity.
  • Accurate indoor positioning is crucial for applications like robotics, augmented reality, and navigation.

Purpose of the Study:

  • To develop and validate a novel hybrid indoor positioning method integrating UWB and visual positioning.
  • To improve the accuracy and continuity of indoor positioning by leveraging advanced machine learning techniques.
  • To enhance pedestrian positioning accuracy in complex indoor environments.

Main Methods:

  • Ultra-wideband (UWB) positioning module utilizing Graph Convolutional Networks (GCN) for feature integration.
  • Visual positioning module employing Bidirectional Gated Recurrent Unit (Bi-GRU) networks for residual compensation.
  • Fusion of UWB and visual positioning coordinates using a Particle Filter (PF) for enhanced accuracy.

Main Results:

  • Single UWB positioning accuracy improved by 55% (error < 0.72 m) compared to Chan-Taylor algorithm.
  • Visual positioning accuracy achieved 0.42 m, a 71% improvement over the Zhang Zhengyou algorithm.
  • Fused positioning achieved 80% accuracy within 0.24 m, with a maximum error of 0.66 m, improving by 56% and 52% over single UWB and visual methods, respectively.

Conclusions:

  • The proposed hybrid UWB and visual positioning method significantly enhances indoor positioning accuracy.
  • Integration of GCN for UWB and Bi-GRU for visual positioning effectively addresses limitations of individual methods.
  • The particle filter-based fusion strategy provides robust and accurate indoor pedestrian positioning.