In the areas you have visited previously you have two estimates of your position one from your frame-to-frame estimates and another from the map you built of the area the first time. You can then solve an optimization problem to bring those two estimates closer together.

In order to find out if you've already visited an area you store a description of the locations in a DB and search through them. The paper says they use a compressed representation of the "maps" and use test time training to optimize the global consistency between their sub maps.

They determine what events/fields are not used and then add filters to your observability provider so you dont pay to ingest them.

This sounds a lot like document search ontop of your specific attributes and you have a custom ranking algorithm.

thanks for the resources !. I've been trying to get a wide view by looking at different algorithms, but I was curious what was actually used in production systems especially for consumer products.

RTABMAP and Cartographer came up in my searches, will definitely give these a closer look to understand how they work.

Right now im starting off with filter based approaches like Particle filter and Kalman filter, but i'd also like to understand how the graph based approaches work.

There’s many different solutions out there but I’m carving out a niche where we deal with complex shift assignment problems.

For example one of our customers has specific union rules that need to be followed when assigning work and we ensure that they are compliant.

Our backend relies on an MIP solver as well as heuristic search to refine plans.

It would be interesting to also have a space where the community could propose research directions.

I've found that it hallucinates tool use for tools that aren't available and then gets very confident about the results.