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The covariance on odometry is kind of tricky, because as your robot moves, the covariance on its position would keep growing without any bounds. To avoid this problem, the robot_pose_ekf resets the covariance of the filter before every sensor update. So the covariance that gets published, is the increase in covariance over the past time interval. Note that the size of this time interval is not always the same; it depends on how fast and when the sensor measurements arrive at the filter node.

You're correct that the covariances of the inputs are on velocity level.