  We argue the case for Gaussian Belief Propagation (GBP) as a strong
algorithmic framework for the distributed, generic and incremental
probabilistic estimation we need in Spatial AI as we aim at high performance
smart robots and devices which operate within the constraints of real products.
Processor hardware is changing rapidly, and GBP has the right character to take
advantage of highly distributed processing and storage while estimating global
quantities, as well as great flexibility. We present a detailed tutorial on
GBP, relating to the standard factor graph formulation used in robotics and
computer vision, and give several simulation examples with code which
demonstrate its properties.
