Example Concept: Sensor fusion utilizes algorithms like the Kalman Filter or Extended Kalman Filter (EKF) to merge data from various sensors. By combining measurements, these algorithms reduce uncertainty and improve the robot's ability to accurately determine its position and orientation. For instance, while GPS provides global positioning, LIDAR offers detailed environmental mapping, and IMUs track motion dynamics, their integration through sensor fusion results in robust localization even in GPS-denied environments.