Kalman filters are mathematical algorithms used to estimate the state of a dynamic system from a series of noisy measurements. They work by combining a prediction model with sensor data to produce more accurate estimates, making them particularly useful in robotics and control systems. These filters help smooth out uncertainties from various sensor types, enhancing the reliability of the information that robots use to navigate and make decisions.
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