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The Improved Method for Indoor 3D Pedestrian Positioning Based on Dual Foot-Mounted IMU System.

Haonan Jia1,2, Baoguo Yu2, Hongsheng Li1

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

Micromachines
|December 23, 2023
PubMed
Summary

This study introduces an improved pedestrian positioning algorithm using dual-foot motion constraints from Micro-Electro-Mechanical System (MEMS) inertial sensors. The new method significantly reduces 3D positioning errors and enhances accuracy without extra power use.

Keywords:
Inertial Measurement Unit (IMU)Kalman filterdual-footinequality constraintpedestrian navigation

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Area of Science:

  • Robotics and Automation
  • Sensor Technology
  • Human-Computer Interaction

Background:

  • Micro-Electro-Mechanical System (MEMS) inertial sensors are vital for foot-mounted pedestrian autonomous positioning due to their size, cost, and power efficiency.
  • Existing systems suffer from heading drift and poor repeatability, limiting their practical application.
  • There is a need for enhanced algorithms to overcome the inherent limitations of MEMS inertial sensors in pedestrian navigation.

Purpose of the Study:

  • To propose an improved pedestrian autonomous 3D positioning algorithm utilizing dual-foot motion characteristic constraints.
  • To enhance the accuracy, continuity, and repeatability of foot-mounted Inertial Measurement Unit (IMU) positioning systems.
  • To address heading drift and improve positioning precision without increasing power consumption.

Main Methods:

  • Implementation of two sets of small-sized IMUs on the left and right feet for a dual-foot autonomous positioning system.
  • Employment of an improved adaptive zero-velocity detection algorithm for enhanced discrimination accuracy across various step speeds.
  • Design of horizontal position update algorithms based on dual-foot motion trajectory constraints and height update algorithms considering dual-foot height differences, especially during stair navigation.

Main Results:

  • Experimental results demonstrate a significant reduction in 3D positioning error by 93.9% in a laboratory environment compared to unconstrained methods.
  • The proposed algorithm effectively re-corrects pedestrian position information in both horizontal and vertical directions at zero velocity.
  • The system shows enhanced accuracy, continuity, and repeatability of positioning.

Conclusions:

  • The dual-foot motion characteristic constraint algorithm effectively mitigates heading drift and improves repeatability in MEMS-based pedestrian positioning.
  • The enhanced zero-velocity detection and dual-foot constraint algorithms provide a robust solution for accurate 3D pedestrian positioning.
  • This approach offers a low-power, high-accuracy solution for foot-mounted IMU navigation systems, suitable for diverse pedestrian activities including stair climbing.