[TooKool4This]

Any location estimator worth its salt is already doing this through the use of a multi-rate EKF. On top of that, it takes into account the vehicle dynamics (i.e. cars can't move perfectly horizontally) to improve estimates.

The "novelty" here is the ML approach although I am not sure if that is particularly novel as well.

[dreamcompiler]

Exactly. I find it exceedingly odd that well-understood techniques like double integration and Kalman Filtering are being replaced by ML black boxes in the hope that ... what? They're going to rediscover what we already know? But in a format where we have no hope of figuring out what they discovered, so we can never bound their domains of acceptable performance?

[cdtwigg]

Sometimes there are other regularities in the data that an ML algorithm can exploit; for example, if it can detect feet strike events in the IMU stream, it can compute an estimate of how fast someone is walking that is independent of integrating accelerations.

[Eddy_Viscosity2]

The benefit of ML over long-standing algos like kalman filtering for integrated navigation systems is they can can give you the occasional surprise when an unexplored edge case happens. People like surprises.

[_kulang]

They can also give you the occasional surprise by diverging from the real trajectory, ex. trains doing barrel rolls

[PaulHoule]

I've used ML to supplement straightforward heuristics before.

For instance, I built a fuzzy matcher for business addresses which was based on hand-rolled heuristics but used logistic regression to train a probability estimator that the match was correct and then hand-evaluated a few thousand matches so that the matcher knew the quality of results it was returning.