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General plane motion, often observed in a rolling wheel, refers to a type of movement where the wheel is simultaneously rotating and translating. This complex motion can be understood by breaking it down into individual components.
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update.

Ran Wei1, Hongda Xu1, Mingkun Yang1

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

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|June 2, 2021
PubMed
Summary

This study introduces an adaptive zero velocity update (AZUPT) method using convolution neural networks to improve pedestrian dead reckoning (PDR) accuracy. The AZUPT method effectively classifies zero velocity update conditions for robust and efficient real-time tracking.

Keywords:
CNNPYNQpedestrian dead reckoningreal-time terminalzero velocity update

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion and Navigation

Background:

  • Pedestrian dead reckoning (PDR) relies on inertial measurement units (IMUs) for navigation.
  • Zero velocity update (ZUPT) is a key calibration technique in PDR.
  • Accurate ZUPT condition determination is challenging due to diverse human walking patterns, impacting tracking precision.

Purpose of the Study:

  • To develop an adaptive zero velocity update (AZUPT) method for robust ZUPT condition classification.
  • To enhance the accuracy and reliability of PDR systems across various individuals and motion types.
  • To validate the real-time performance and computational efficiency of the AZUPT method.

Main Methods:

  • An adaptive zero velocity update (AZUPT) model utilizing convolution neural networks (CNNs) was designed.
  • The AZUPT model was implemented on a Zynq-7000 System-on-Chip (SoC) platform for real-time processing.
  • Extensive real-world experiments involved 60 participants across three distinct environmental scenarios.

Main Results:

  • The AZUPT method demonstrated robustness across different individuals and motion types.
  • Real-time implementation on the Zynq-7000 SoC confirmed computational efficiency and performance superiority.
  • The system achieved consistent performance in diverse environments, indicating broad applicability.

Conclusions:

  • The proposed AZUPT method significantly improves ZUPT condition classification for PDR.
  • The system offers a portable and effective solution for accurate PDR in various real-world situations.
  • AZUPT enhances navigation accuracy by adapting to individual walking dynamics and environmental conditions.