One important thing I would like to add to @LemFromTheLib and @Mentor_355U explanations is that GPS sensor seems to be very sensitive to the minor irregularities in the dimensions and angles of the particular field setup as well as to the exact location and orientation of where sensor is mounted on the robot.
So, it would be necessary to calibrate it for every individual field setup to get good accuracy.
I don’t think the GPS sensor itself has that feature, therefore teams would have to figure out pre-calibrated biases / corrections for their robot on each individual field and pass them as initial setup parameters for the filter run on that field.
Another question for the sensor firmware developers (@jpearman ?) would be if the image snapshots taken by the sensor and used for position calculation are strictly simultaneous or sequential taken over some period of time?
In the latter case, I would assume accuracy of the results will degrade if the robot was moving (translating and rotating) and it’s velocity was not accounted while calculating GPS solution.
Finally, if you are using Kalman Filter and predicting trajectory of a moving object, it is not as important to have high frequency / low latency measurements, as it is to know exact time of when that measurement was taken.
For example, you could get much accurate results if you know exact time of a measurement (+/- 1 msec) taken once every 300 msec arriving up to 100 msec after it was completed, rather than if you have sensor returning a measurement every 50 msec with up to 10 msec delay, but you have no idea when this measurement was actually taken within the last 60 msec. The final position estimation error will be proportional to the robot speed.