Multi-Sensor Fusion Framework for Reliable Localization and Trajectory Tracking of Mobile Robot by Integrating UWB,...

Area of Science:

• Robotics
• Sensor Fusion
• Indoor Navigation

Background:

• Accurate indoor localization is crucial for mobile robot navigation.
• Existing methods like Ultra-Wideband (UWB) trilateration can be prone to noise and drift.

Purpose of the Study:

• To develop and validate a multi-sensor fusion framework for enhanced indoor localization and trajectory tracking.
• To improve the robustness and accuracy of differential-drive mobile robot positioning.

Main Methods:

• Integration of Ultra-Wideband (UWB) trilateration, wheel odometry, and Attitude and Heading Reference System (AHRS) data.
• Application of a Kalman filter for sensor data fusion.
• Experimental validation using closed-loop trajectory trials.

Main Results:

• The proposed fusion method significantly reduces noise and corrects odometry drift compared to UWB-only localization.
• Enhanced consistency and lower Dynamic Time Warping (DTW) distances were observed across trajectory repetitions.
• Demonstrated superior positioning accuracy and robustness in indoor environments.

Conclusions:

• The multi-sensor fusion framework effectively improves indoor localization and trajectory tracking for mobile robots.
• The system is suitable for real-time navigation applications requiring high accuracy and reliability.
• Sensor fusion mitigates limitations of individual sensors, leading to more dependable robot positioning.