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So I have yet to run your test package, and I can't speak at all to the amcl issues, but re: the EKF covariance, for any given time step, there are three covariance matrices that matter:

The estimate error covariance (the error for the entire state)
The process noise covariance (how much noise we add to the filter every time we do a prediction)
The measurement covariance (the error in the measurement itself)

At every time step, we make a prediction about where we should be, given the motion model, and the also make a prediction about what the estimate error covariance matrix will be. The equation for prediction is this:

P = JPJ' + Q

Q is the process noise covariance matrix. In general, if your pose covariance is growing too quickly, you'll want to check the values for your pose AND twist variables in the process_noise_covariance parameter for the EKF. The odometry twist covariances are also important, so you can tune those as well.

So I have yet to run your test package, and I can't speak at all to the amcl issues, but re: the EKF covariance, for any given time step, there are three covariance matrices that matter:

The estimate error covariance (the error for the entire state)
The process noise covariance (how much noise we add to the filter every time we do a prediction)
The measurement covariance (the error in the measurement itself)

P = JPJ' + Q

EDIT 1 in response to comment question:

In many Kalman Filter formulations, I believe the time step is assumed to be constant, so Q is also constant. In the state estimation nodes in r_l, Q gets multiplied by the time delta, so the process_noise_covariance parameter is how much process noise you'd accumulate per second. This is necessary in order to cope with the measurements arriving at uncertain intervals. Tuning the values for Q is not easy, and, from what I've seen, is typically done by watching the error growth and determining if it's reasonable (i.e., hand tuning).