[jandrewrogers]

You can't build repeatable models of space for high-accuracy registration with big drone or car hardware either, I've worked with both. The geometry of space may rhyme but it never repeats. Those links don't address registration.

If you measure the environment with high-precision and use that to construct a geometric model of the space, and then come back a week later and measure it with the same instruments, the two spaces won't be congruent even for objects we normally think of as invariant, and the variability is sometimes surprising in magnitude. The noise floor for repeatable measurement out in the physical world is centimeters in most cases, regardless of the instrument precision used to measure it. This isn't a problem if you don't need particularly high-precision but people are inventing applications that do.

The software challenge is trying to position relative to previous measurements of the same space when the myriad positioning cues are contradictory. Knowing which of the totality of cues are relevant in context so that the software can appropriately adapt its positioning behavior to the change in geometry is the part that is usually deemed AI-complete by the people I know that have been working in the space a long time. There are many infamous example cases of humans being able to correctly register contradictory positioning information in context that we don't know how to algorithm our way out of currently.

Some of the drone work we did was actually measuring how the geometry of "fixed" spaces varies over time. The world around us moves a lot more than humans can perceive.

[Animats]

OK. I've never worked tighter than 15cm, for automatic driving, so I haven't seen that.