Kalman filtering is a powerful mathematical technique used in robotics for state estimation in dynamic systems. It operates on the principle of recursively estimating the state of a system by minimizing the mean of the squared errors, thereby providing a statistically optimal estimate. The filter combines measurements from various sensors, such as GPS, accelerometers, and gyroscopes, to produce a more accurate estimate of the robot's position and velocity.
The Kalman filter works in two main steps: Prediction and Update. During the prediction step, the current state is projected forward in time based on the system's dynamics, represented mathematically as:
In the update step, the predicted state is refined using new measurements:
where is the Kalman gain, which determines how much weight to give to the measurement . By effectively filtering out noise and uncertainties, Kalman filtering enables robots to navigate and operate more reliably in uncertain environments.
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