Here's the thing: Dellaert loves to write about SLAM from a viewpoint of probabilities and densities. I think he likes to see himself as a mathematician. However at the end of the day he's working with Gaussian noise assumptions, just like everyone else, and the solution is obtained by some form of Least Squares, again, like everyone else.
I would really like it if he just cut all the probabilistic thicket and went straight to the core of how his methods improve the SOTA just in terms of how the friggin Least Squares problem is set up and solved (e.g. like here [1]). But I guess that would probably take away most of the "magic".
To use another example: Let's say you understand and prefer to use QR factorization for some linear algebra use case. That doesn't mean that Cholesky or SVD are somehow "worthless" tools -- they just provide other useful insights or applications.
I personally find Dellaert's decomposition of the problem useful. It doesn't hurt that he was one of the early SLAM "inventors" (along with the likes of Fox, Burguard, et al). And since the topic of the post is (literally) probabilistic graphical models, it seems extremely relevant to this thread.
(I read through the article you linked. I do not agree that it is an "easier" or "simpler" formulation.)
But Dellaert didn't invent Factor Graphs, nor how to map a Factor Graphs to a Least Squares problem. Others did that way before him.
My criticism is directed towards his style of writing about SLAM in terms of probabilities and densities, which, IMO, is moot since at the end of the day he's just using Gaussians as everyone else and hence everything boils down to a Least Squares problem again. His probabilistic viewpoint is just a detour, a distraction at that point.
If he contributed a different kind of solver that could deal with arbitrary distributions then his probabilistic viewpoint would make a whole lot more sense but as long as it's just Gaussians I just don't see the point.
SLAM at a multi-city scale can use mathematical trickery to improve runtime. Sure, a new solver would help - but we don't need it as long as there is another trick up the mathematician's sleeve. Oh, and it's harder to come up with a new solver / technique.
Plus a segment of academia loves this type of research.
Well you need some kind of model to optimize, and finding the perfect one is a vastly more difficult problem that probably begins to incorporate some stuff from information theory (which starts to become rather a different field).
I don't actually see the issue with researchers working on algorithms to solve the Gaussian noise versions of these problems — they can usually be extended to some other model relatively easily (for instance, rotation synchronization via Riemannian manifolds can incorporate a Pseudo-Huber loss function without much difficulty).
Also, what is SOTA?