  Scene graphs represent the key components of a scene in a compact and
semantically rich way, but are difficult to build during incremental SLAM
operation because of the challenges of robustly identifying abstract scene
elements and optimising continually changing, complex graphs. We present a
distributed, graph-based SLAM framework for incrementally building scene graphs
based on two novel components. First, we propose an incremental abstraction
framework in which a neural network proposes abstract scene elements that are
incorporated into the factor graph of a feature-based monocular SLAM system.
Scene elements are confirmed or rejected through optimisation and incrementally
replace the points yielding a more dense, semantic and compact representation.
Second, enabled by our novel routing procedure, we use Gaussian Belief
Propagation (GBP) for distributed inference on a graph processor. The time per
iteration of GBP is structure-agnostic and we demonstrate the speed advantages
over direct methods for inference of heterogeneous factor graphs. We run our
system on real indoor datasets using planar abstractions and recover the major
planes with significant compression.
