Example Concept: Sensor fusion in robotics typically involves integrating data from sources such as GPS, IMU (Inertial Measurement Unit), LIDAR, and cameras. Each sensor provides different types of information — GPS offers global position, IMU provides orientation and acceleration, LIDAR gives distance measurements, and cameras capture visual data. By using algorithms like the Extended Kalman Filter (EKF) or Particle Filter, these diverse data inputs are combined to produce a more accurate and robust estimate of the robot's state, compensating for individual sensor noise and drift.