Example Concept: The Kalman filter operates in two main steps: prediction and update. During the prediction step, it uses the system's previous state and a motion model to predict the current state. In the update step, it incorporates new sensor measurements to correct the predicted state, reducing the overall estimation error. This iterative process allows the filter to continuously refine the robot's estimated position and velocity, even in the presence of noise and uncertainties in sensor data.