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Autonomous Fingerprinting and Large Experimental Data Set for Visible Light Positioning.

Tyrel Glass1, Fakhrul Alam1, Mathew Legg1

  • 1Department of Mechanical & Electrical Engineering, SF&AT, Massey University, Auckland 0632, New Zealand.

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Summary
This summary is machine-generated.

This study introduces an autonomous data collection method for Visible Light Positioning (VLP) using Virtual Reality (VR) tracking. Machine learning, particularly ridge regression, offers superior localization accuracy with extensive datasets.

Keywords:
Indoor LocalizationIndoor Positioning Systems (IPS)Virtual Reality (VR)Visible Light Positioningfingerprintground truth

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Area of Science:

  • Indoor positioning systems
  • Optical wireless communication
  • Robotics and automation

Background:

  • Visible Light Positioning (VLP) offers a promising alternative for indoor localization.
  • Traditional data collection for VLP is labor-intensive and limits dataset scale.
  • Accurate ground truth is crucial for evaluating VLP algorithms.

Purpose of the Study:

  • To develop an autonomous method for collecting Visible Light Positioning (VLP) data.
  • To conduct a comprehensive investigation of VLP accuracy using a large experimental dataset.
  • To compare the performance of Machine Learning (ML) and channel model-based positioning techniques.

Main Methods:

  • An autonomous data collection system utilizing consumer-grade Virtual Reality (VR) tracking for precise ground truth.
  • Deployment of a mobile robotic platform equipped with a VLP receiver and VR tracker.
  • Collection of Received Signal Strength (RSS) data at 7344 locations within a controlled test environment.

Main Results:

  • The VR ground truth system achieved median and 90th percentile errors of 4.24 mm and 7.35 mm, respectively.
  • Ridge regression demonstrated the highest accuracy among tested ML techniques.
  • ML-based positioning outperformed model-based methods with large datasets, while model-based methods were superior with small datasets.

Conclusions:

  • Autonomous data collection significantly enhances the scale and accuracy of VLP experiments.
  • Machine learning techniques, especially ridge regression, provide high localization accuracy for VLP with sufficient data.
  • The choice between ML and model-based positioning depends on the available data volume for calibration and training.