Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach ^†

Area of Science:

• Robotics
• Localization and Mapping
• Sensor Fusion

Background:

• Autonomous vehicles are crucial for smart warehouses and factories, demanding efficient indoor localization.
• Current localization methods face challenges in accuracy, affordability, and real-time application for mobile agents.

Purpose of the Study:

• To design an Extended Kalman Smoother for simultaneous localization and trajectory reconstruction of indoor mobile agents.
• To evaluate the performance of the proposed method using UHF-RFID passive technology through simulations and experimental analysis.

Main Methods:

• Sensor-fusion approach combining UHF-RFID passive technology with an Extended Kalman Smoother.
• Extensive simulations to determine optimal smoother window length and assess the impact of missing measurements.
• Monte Carlo simulations to evaluate accuracy across various vehicle trajectories and velocities.
• Experimental validation using a unicycle wheeled robot in an indoor environment.

Main Results:

• The Extended Kalman Smoother effectively localizes mobile agents and reconstructs their trajectories.
• Simulations demonstrated the method's robustness to missing measurements and varying operational parameters.
• Experimental analysis yielded position and orientation root mean square errors of 15 cm and 0.2 rad, respectively.

Conclusions:

• The proposed Extended Kalman Smoother provides an effective and accurate solution for indoor vehicle localization and trajectory reconstruction.
• UHF-RFID passive technology integrated with sensor fusion offers a promising approach for real-time applications in autonomous systems.
• The method's performance in real-world scenarios validates its potential for smart warehouse and factory environments.