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In Simultaneous Localization and Mapping: Part I, the Extended Kalman Filter is described on page 5. I'm confused about where it says "$w_k$ are additive, zero mean uncorrelated Gaussian motion disturbances with covariance $Q_k$".

To state an assumption: I think that $w_k$ is a single motion disturbance at time k, although I'm not positive about this.

My question is, given that covariance is the measure of how much two random variables change together, what are the two random variables for $Q_k$?

Some additional thoughts:

I considered the possibility that this could be the covariance between the wheels, but there could be three wheels, and covariance is only between two variables. (Am I wrong about this?) I also considered that it might be between the x and y coordinates, but what about the angles?

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up vote 1 down vote accepted

For the Kalman Filter, $w_{k}$ acts like the noise that corrupts your observations. In a perfect world, you could observe the motion without any problems, but in reality, the sensor or measuring device isn't perfect so you get random extra jitters or artifacts that are just random error. Since you're estimating many different parameters of a model, though, some of the errors might affect two or more different variables. That's why you have to consider the covariance matrix, $Q_{k}$. It tells you what that random-noise-structure looks like for your given problem.

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I had thought the covariance $Q_k$ was a scalar, not a matrix describing the relationship between all the parameters of the model. – munk Mar 15 '12 at 18:59
Do you have any suggestions for Q matrix values for vehicle movement (normal and sidewise) in a Constant Velocity (CV) model? I would appreciate any pointers to any articles. – Dimme Jan 29 '14 at 1:31

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