Interesting, as they mention, it looks like the main advantage to an approach like this is that it can make use of "special events" (e.g. stop/starts, bumps, turning) which can null out the drift.

Combining this with other covariates/sensors could also provide more correction and better detection of "special events".

I wonder if something like this applied on top of traditional methods that incorporate the dynamics would make a better approach since you get the advantage of using the known dynamics + learnable "special event" corrections.