FutureMapping 2: Gaussian Belief Propagation for Spatial AI
Artificial Intelligence
2022-11-08 v2 Computer Vision and Pattern Recognition
Distributed, Parallel, and Cluster Computing
Robotics
Abstract
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.
Keywords
Cite
@article{arxiv.1910.14139,
title = {FutureMapping 2: Gaussian Belief Propagation for Spatial AI},
author = {Andrew J. Davison and Joseph Ortiz},
journal= {arXiv preprint arXiv:1910.14139},
year = {2022}
}