Example Concept: Sensor fusion in robotics often involves integrating data from sources like IMUs, GPS, LiDAR, and cameras to improve localization. The Kalman Filter is a common algorithm used for this purpose, as it efficiently estimates the robot's state by predicting and updating the position based on sensor inputs. This process helps in reducing uncertainty and increasing the robustness of the localization system, especially in dynamic or GPS-denied environments.