The maths behind this kind of fusion of different data sources is also pretty beautiful. Kalman filters (https://www.bzarg.com/p/how-a-kalman-filter-works-in-pi

The maths behind this kind of fusion of different data sources is also pretty beautiful. Kalman filters (https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/ https://www.bzarg.com/p/how-a-kalman-filter-works-in-picture...) are similar to the sensor fusion community what RNNs are to machine learning; simply stick all your data sources in with their uncertainties, and put pops a corrected estimate (in this case, position). It makes it much easier to combine data sources with rapid refresh rate but high integral error (IMU, gyroscopes, ...) with GPS/celestial navigation.