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Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker

Humayun Khan1,2, Adrian Clark3, Graeme Woodward2

  • 1Human Interface Technology Laboratory, University of Canterbury, Christchurch 8041, New Zealand.

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Summary

This study introduces a new pedestrian indoor positioning system using sensor fusion. It improves accuracy by accounting for different walking types, enhancing after-action reviews for first responders.

Keywords:
extended Kalman filterfiducial marker trackingfoot-mounted inertial sensorvisual-inertial sensor fusionzero-velocity update

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Area of Science:

  • Robotics and Sensor Fusion
  • Human-Computer Interaction
  • Navigation Systems

Background:

  • Existing indoor positioning systems for training require extensive active infrastructure, limiting their use in remote areas.
  • First responders need accurate after-action reviews during training exercises, especially in varied environments.

Purpose of the Study:

  • To develop a novel, infrastructure-light pedestrian indoor positioning system for first responder training.
  • To enhance positioning accuracy by considering variations in pedestrian locomotion (e.g., forward, backward, sideways walking).

Main Methods:

  • Sensor fusion of a foot-mounted inertial measurement unit (IMU) and vision-based fiducial marker tracking.
  • Utilizing an extended Kalman filter (EKF) for loosely coupling IMU and vision data.
  • Developing a motion model from IMU data and a measurement model from vision system data.

Main Results:

  • The system achieves near-meter level accuracy with discrete corrections over a 250 m trajectory.
  • Positioning accuracy varies with walking type: 0.55 m (forward), 1.05 m (backward), and 1.68 m (sideways) at 90% confidence.
  • The approach balances accuracy and power consumption by adaptively activating camera tracking.

Conclusions:

  • The proposed system offers a viable solution for accurate indoor positioning in training scenarios, using passive infrastructure.
  • Accounting for different walking gaits significantly improves the accuracy of pedestrian dead reckoning.
  • This technology enhances the realism and effectiveness of after-action reviews for first responders.