To model the robot position we wish to know its x and y coordinates and its orientation. These three parameters can be combined into a vector called a state variable vector. The robot uses beacon distance and angle measurements and locomotion information about how far it has walked to calculate its position. As with any real system, these measurements include a component of error (or noise). If trigonometry is used to calculate the robot s position it can have a large error and can change significantly from frame to frame depending on the measurement at the time. This makes the robot appear as if it is "jumping" around the field. The Kalman Filter is a smarter way to integrate measurement data into an estimate by recognising that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate.

Applications of the Kalman Filter slgorithm to robot localization and.pdf

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