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Marker-Based Multi-Sensor Fusion Indoor Localization System for Micro Air Vehicles.

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Summary

This study introduces a novel multi-sensor fusion algorithm for indoor localization using ArUco markers. The system achieves centimeter-level accuracy and reduces position drift, offering a low-cost solution for Micro Aerial Vehicles and robotics.

Keywords:
ArUco markerMicro Aerial Vehiclefederated filterindoor localization

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Sensor Fusion

Background:

  • Accurate indoor localization is crucial for Micro Aerial Vehicles (MAVs) and robotic systems.
  • Existing localization methods often suffer from drift, distortion, or high costs.
  • ArUco markers offer a robust visual fiducial system for localization.

Purpose of the Study:

  • To develop a novel multi-sensor fusion indoor localization algorithm utilizing ArUco markers.
  • To enhance map accuracy and reduce localization drift through online map correction and sensor fusion.
  • To present a small-size, low-cost, and easily implementable localization system.

Main Methods:

  • Online ArUco marker map building and correction using Grubbs criterion and K-mean clustering.
  • Multi-sensor information fusion employing a federated Kalman filter, integrating data from ArUco markers, optical flow, ultrasonic, and inertial sensors.
  • Implementation on a hardware platform comprising Raspberry Pi Zero and STM32 microcontrollers.

Main Results:

  • The proposed algorithm achieves centimeter-level accuracy in mapping and positioning.
  • Speed estimation performance surpasses that of Px4flow.
  • The system effectively reduces position drift, even during prolonged marker signal loss.
  • The system demonstrates robustness and potential for expansion with additional sensors.

Conclusions:

  • The developed ArUco marker-based multi-sensor fusion system provides accurate and reliable indoor localization.
  • The low-cost and compact design makes it suitable for resource-constrained platforms like MAVs.
  • The system's modularity allows for future integration of diverse sensors to further enhance localization performance.