Example Concept: Sensor fusion in robotics typically involves integrating data from various sensors like GPS, IMUs, and LIDAR to enhance localization accuracy. By using algorithms such as the Kalman Filter or Extended Kalman Filter, sensor fusion processes the noisy and partial data from each sensor to produce a more accurate and reliable estimate of the robot's position and orientation. This approach compensates for the weaknesses of individual sensors, such as GPS signal loss or IMU drift, thereby improving the overall localization performance.