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Usually you estimate the covariance matrix based on the accuracy of your sensors. If you have reliable sensors, then your covariance should be smaller. You can do this by trial and error, for example, you could drive your robot 10 meters in a straight line (open loop control), then measure the error (between where the robot thinks it is, and where it actually is in real life), the actual position of the robot should be within the covariance ellipse which is centered around the robot's estimated position. You want your covariance to be as realistic as possible, so if you have a really large covariance matrix, then obviously the the true position of the robot will fall within the bound, but this is not good because it can cause data association failure if you implement a SLAM algorithm.