Example Concept: Sensor fusion in robotics involves integrating data from various sensors using algorithms like the Kalman Filter or Particle Filter. These algorithms process inputs from sources like GPS (for global positioning), IMUs (for motion and orientation), and cameras (for visual cues) to produce a cohesive and accurate estimate of the robot's location. By cross-verifying and correcting sensor data, sensor fusion reduces errors and increases the reliability of the localization system.