Example Concept: Sensor fusion in robotics typically involves integrating data from various sensors using algorithms like the Kalman Filter or Particle Filter. These algorithms combine measurements from sensors such as GPS, IMUs, and LIDAR to refine the robot's pose estimation. By fusing data, the system can correct for errors and noise inherent in individual sensors, resulting in a more accurate and reliable localization solution.