Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Area of Science:

• Robotics
• Wireless Navigation Systems
• Localization Technologies

Background:

• Indoor localization is crucial for numerous applications, driving demand for advanced positioning techniques.
• Fingerprinting, particularly using mobile robots, is a growing area in indoor positioning.
• Existing methods often require complex architectures or extensive calibration.

Purpose of the Study:

• To present a simple, cost-effective, and accurate auto-fingerprinting method for indoor localization.
• To leverage Radio Frequency Identification (RFID) technology with a two-wheeled mobile robot.
• To address displacement errors in robot navigation for improved localization accuracy.

Main Methods:

• An auto-fingerprinting localization system using RFID and a two-wheeled robot was developed.
• Robot navigation displacement errors were assessed and corrected.
• The Dual One Slope with Second Order propagation Model (DOSSOM) was used for environmental calibration and tag-reader link modeling.
• The system employs a two-stage approach: offline calibration and online position estimation via DOSSOM and multilateration.

Main Results:

• The proposed auto-fingerprinting method achieved a positioning error of 1.22 meters at the 90% cumulative distribution function.
• The system operated effectively with only four active RFID tags.
• The localization architecture demonstrated reduced complexity.

Conclusions:

• The developed method offers a simple, cost-effective, and accurate solution for indoor localization using RFID and mobile robots.
• The integration of displacement error correction and the DOSSOM model enhances positioning precision.
• This approach provides a viable alternative for indoor positioning systems with limited resources and complexity.