Example Concept: Sensor fusion in robotics often utilizes algorithms such as the Extended Kalman Filter (EKF) or Particle Filter to merge data from various sensors. These algorithms account for the uncertainties and noise inherent in each sensor's measurements, thus refining the robot's state estimation. By leveraging the complementary strengths of different sensors, such as the precision of LIDAR and the broad coverage of cameras, sensor fusion significantly enhances the localization accuracy and reliability, especially in dynamic or GPS-denied environments.