Kalman filtering techniques are mathematical algorithms used for estimating the state of a dynamic system from a series of incomplete and noisy measurements. These techniques are crucial for predicting future states and minimizing estimation errors, making them essential in various applications, especially in fields like robotics and autonomous vehicles. By processing incoming sensor data iteratively, Kalman filters help refine estimates of position and velocity, which are vital for tasks such as depth estimation and data integration with cloud computing systems.
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