Example Concept: Sensor fusion in robotics typically involves integrating data from sources such as IMUs (Inertial Measurement Units), GPS, LiDAR, and cameras. By using algorithms like the Extended Kalman Filter (EKF) or Particle Filters, the system can continuously update its state estimate, compensating for sensor noise and drift. This results in a more accurate and robust localization, essential for tasks like navigation and mapping in dynamic environments.