Example Concept: Sensor fusion in robotics typically involves integrating data from IMUs, GPS, LIDAR, and cameras using algorithms like the Extended Kalman Filter (EKF) or Particle Filters. These algorithms process the sensor inputs to estimate the robot's pose with higher precision than any single sensor could achieve alone. By continuously updating the state estimate with new sensor data, sensor fusion reduces uncertainty and improves the robustness of the localization system.