[pietroglyph]

An interesting side point is that the graph optimization approach used here is somewhat similar to modern graph-based visual SLAM

The graph in the article can be seen as a factor graph. VSLAM systems usually have a (kind of bipartite) factor graph with vertices/variables that are either keyframes or ‘good’ features, with edges/factors between features and the frames that see them and between adjacent frames; each of these edges are the factors in the graph. This structure results in very large but sparse graphs, and there are factor graph optimization libraries that take advantage of this sparsity (e.g. g2o or GTSAM.) These libraries also use specialized optimization techniques for some of the nonlinear manifolds (e.g. SO(3)) that arise in SLAM problems.