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Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation.

Bibiana Fariña1, Jonay Toledo1, Jose Ignacio Estevez1

  • 1Computer Science Departament, University of La Laguna, Avd Fco Sanchez, Fisica y Matematicas, 38200 La Laguna, Spain.

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Summary
This summary is machine-generated.

This study introduces an autonomous wheelchair localization module using variable sensor accuracy estimation. This dynamic approach improves positioning accuracy compared to traditional methods, enhancing autonomous navigation safety.

Keywords:
localizationodometricroboticssensor fusion

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Area of Science:

  • Robotics
  • Sensor Fusion
  • Autonomous Systems

Background:

  • Accurate localization is critical for autonomous wheelchair navigation.
  • Traditional methods often use static sensor accuracy values, which can be unreliable.
  • Integrating multiple sensors (odometers, laser scanners, IMU, Doppler speed sensors) presents challenges in estimating individual sensor accuracy.

Purpose of the Study:

  • To develop and evaluate a novel localization module for autonomous wheelchairs.
  • To implement variable covariance estimation for each sensor based on data quality.
  • To improve the overall localization accuracy and robustness of autonomous wheelchairs.

Main Methods:

  • Utilized a sensor suite including odometers, laser scanners, Inertial Measurement Unit (IMU), and Doppler speed sensors.
  • Implemented variable covariance estimation for each sensor, adapting to real-time data quality.
  • Employed Doppler speed sensing to refine odometric localization covariance.
  • Applied Lidar scan matching for position estimation, using matching quality to determine accuracy.

Main Results:

  • The proposed localization module demonstrated superior positioning accuracy compared to traditional static covariance methods.
  • Variable covariance estimation based on data quality significantly enhanced localization robustness.
  • Real-world prototype testing validated the effectiveness of the dynamic sensor fusion technique.

Conclusions:

  • Dynamic, data-quality-dependent covariance estimation is a key advancement for multi-sensor localization systems.
  • The developed localization module offers a more reliable and accurate solution for autonomous wheelchair navigation.
  • This approach provides a foundation for more sophisticated and dependable autonomous mobility solutions.