I totally understand where you are coming from. I read multiple tutorials and even implemented Kalman filters in Python, Matlab, and C++ for a balancing robot p

I totally understand where you are coming from. I read multiple tutorials and even implemented Kalman filters in Python, Matlab, and C++ for a balancing robot project. I never really understood what was happening even though I knew what the Kalman filter is used for and when I need it. The thing that "clicked" for me is that multiple measurements are always more accurate than a single measurement. That's why there is the feedback loop that derives the predicted value and the uncertainty that is associated with it. By combining the predicted value and the measured value (and their uncertainties), you get a more accurate measurement. Then you can use this more accurate measurement to "correct" your predictions so that the next prediction has even less uncertainty. This perpetuality is why Kalman filters lead to the "true" state.