Automating the Calibration of Visible Light Positioning Systems.

Area of Science:

• Robotics
• Computer Vision
• Indoor Positioning Systems

Background:

• Visible light positioning (VLP) is a key technology for indoor navigation.
• Accurate calibration, determining light source location and identity, is crucial for VLP system performance.
• Manual calibration is time-consuming, labor-intensive, and prone to errors.

Purpose of the Study:

• To develop and evaluate novel autonomous methods for collecting VLP calibration data using a mobile robot.
• To enhance the efficiency and accuracy of the VLP system calibration process.
• To reduce reliance on manual data collection for VLP system deployment.

Main Methods:

• Proposed two novel autonomous calibration methods for mobile robots: semi-autonomous and fully-autonomous.
• Semi-autonomous calibration leverages prior knowledge of LED locations and environmental maps.
• Fully-autonomous calibration operates without any prior environmental or sensor information.

Main Results:

• Both proposed autonomous methods demonstrated higher accuracy compared to manual steering.
• Semi-autonomous calibration significantly reduced data collection time.
• Fully-autonomous calibration achieved comparable completion time to manual methods while improving accuracy.

Conclusions:

• Autonomous mobile robot-based data collection offers a more efficient and accurate approach to VLP system calibration.
• The developed methods, particularly semi-autonomous calibration, can substantially streamline VLP system deployment.
• Future research can build upon these methods for fully automated indoor positioning system calibration.