The factor graph representation and analysis lends itself to simplified understanding and reasoning about the relationship between poses, measurements, loop closures, etc. in a way that is simple and intuitive. Dellaert then uses that intuitive structure to show how to setup the problem in a way that can be efficiently computed using linear algebra.
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.)
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.)