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Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning.

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This study presents an automated method for calibrating radio signal strength (RSS) fingerprinting positioning systems using robotic platforms and machine learning. The approach achieves accurate indoor localization, with median errors as low as 0.4 meters for trajectory tracking.

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Area of Science:

  • Robotics
  • Machine Learning
  • Indoor Positioning Systems

Background:

  • Radio Signal Strength (RSS) fingerprinting is a common technique for indoor localization.
  • Manual calibration of RSS fingerprinting systems is time-consuming and labor-intensive.
  • Automated calibration methods are needed to improve efficiency and accuracy.

Purpose of the Study:

  • To develop and evaluate an automated method for calibrating RSS-fingerprinting-based positioning systems.
  • To integrate robotic platforms for data collection and machine learning for model training.
  • To assess the performance of different machine learning models for radio map calibration.

Main Methods:

  • Utilized a robotic platform for automated data acquisition in the system environment.
  • Employed GraphSLAM for environment mapping and radio map calibration.
  • Trained and compared four machine learning models: log-distance path loss, Gaussian Process Regression, Artificial Neural Network, and Random Forest Regression.
  • Tested the system in a BLE-based indoor localization setup within an apartment.

Main Results:

  • The automated calibration method successfully enabled positioning with accuracy comparable to existing literature.
  • The Artificial Neural Network model achieved a median robot positioning error of 0.87 meters.
  • The median trajectory error for a walking person localization scenario was 0.4 meters.

Conclusions:

  • The proposed automated calibration method, integrating robotics and machine learning, is effective for RSS-fingerprinting positioning systems.
  • The study demonstrates the feasibility of achieving high-accuracy indoor localization through automated calibration.
  • The evaluated machine learning models provide viable solutions for radio map calibration and positioning.