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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Indoor Pedestrian Location via Factor Graph Optimization Based on Sliding Windows.

Yu Cheng1, Haifeng Li2, Xixiang Liu1

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

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|September 13, 2025
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Summary
This summary is machine-generated.

This study introduces an advanced indoor pedestrian positioning method using smartphone sensors and a Transformer+TCN model for precise step length estimation. The new approach significantly enhances positioning accuracy and reliability in challenging indoor environments.

Keywords:
FGOGNSSPDRTCNtransformer

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Area of Science:

  • Robotics and Intelligent Systems
  • Sensor Fusion and Navigation
  • Machine Learning for Signal Processing

Background:

  • Global Navigation Satellite Systems (GNSS) are ineffective indoors due to signal obstruction and multipath effects.
  • Smartphones equipped with micro-electro-mechanical system (MEMS) sensors offer potential for indoor localization.
  • Accurate indoor pedestrian positioning is crucial for various applications.

Purpose of the Study:

  • To develop a high-precision and reliable indoor pedestrian dead reckoning (PDR) method using smartphone sensors.
  • To improve step length estimation accuracy and robustness.
  • To enhance overall indoor positioning performance.

Main Methods:

  • Utilized inertial measurement unit (IMU)/PDR pre-integration to mitigate inertial navigation divergence.
  • Developed a novel step length estimation algorithm combining Transformer and Temporal Convolutional Network (TCN) architectures.
  • Implemented sliding window factor graph optimization (SW-FGO) using accelerometer, gyroscope, and magnetometer data for accurate pose estimation.

Main Results:

  • The proposed SW-FGO fusion method improved positioning accuracy by 29.68% compared to traditional FGO.
  • The Transformer+TCN based step length estimation algorithm reduced absolute position error by 42.15% in pocket mode versus LSTM.
  • The Transformer+TCN model achieved a step length estimation error of 1.61%, improving accuracy by 24.41%.

Conclusions:

  • The proposed Transformer+TCN and SW-FGO based PDR method significantly enhances indoor pedestrian positioning accuracy and reliability.
  • The novel approach effectively addresses the limitations of traditional methods in complex indoor environments.
  • This technology holds promise for improving navigation and location-based services indoors.