Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FutureMapping 2: Gaussian Belief Propagation for Spatial AI   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.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc