At University I was involved in a project trying to estimate states, solely based on inertial sensors. It is true, that estimates of states will drift off the

At University I was involved in a project trying to estimate states, solely based on inertial sensors. It is true, that estimates of states will drift off the ground truth very fast, but I don't think that ML approaches are the best solution to these kind of problem. In our tests we tried to estimate the position of a bicycle solely with an iPhone taped to the handlebar. My first idea was to update the state based on the fact, that the handlebar would always point in the riding direction. And the second idea was to update the altitude of the estimation based on a heightmap of the environment. Of course, without these two approaches the estimation would drift off rapidly. But the first approach was enough to confine our estimation to the general area of riding (at least for the time of measuring). With this in place the most dirft would come from noisy gyrometer measurements and the angle of corners was not always right. The position estimation did not work as nice as I hoped. But based on the fact, that the estimation did not drift away at breakneck speeds, I concluded that at least you could use the algorithm for a speedometer. If you would add a "zero velocity update" based on unchanged GPS positions it would work even better. It was incredibly easy to eliminate a major part of noise in this problem. It is such a trivial idea, that I was struck when I first had a look at the great results. And I am sure that a machine learning approach would just learn the transformation between the IMU and the car and then ignore measurements that are implying that the car would drive in impossible directions.