Example Concept: Sensor fusion for robot localization often employs techniques like Kalman Filters, Particle Filters, or SLAM algorithms to merge data from different sensors. These methods help in reducing noise, compensating for individual sensor weaknesses, and providing a more robust estimate of the robot's position and orientation. The challenge lies in ensuring that the data from each sensor is accurately time-stamped and aligned, which is critical for effective fusion and accurate localization.