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FutureMapping 2: Gaussian Belief Propagation for Spatial AI

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arxiv 1910.14139 v2 pith:JW2QGX73 submitted 2019-10-30 cs.AI cs.CVcs.DCcs.RO

Andrew J. Davison , Joseph Ortiz This is my paper · ORCID
classification cs.AIcs.CVcs.DCcs.RO
keywords beliefdistributedgaussianpropagationspatialadvantagealgorithmicargue
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Adaptive Solvers for Distributed Factor Graph Optimization on Matrix Lie Groups

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A learned feedback policy replaces manual parameter tuning in distributed Riemannian optimization over matrix Lie groups, achieving lower objective values on multi-robot mapping benchmarks.

  2. Gaussian Belief Propagation Network for Depth Completion

    cs.CV 2026-01 conditional novelty 6.0 of 10

    Depth completion via a learned Markov random field solved with Gaussian belief propagation reports state-of-the-art RMSE on NYUv2 and best iRMSE on KITTI, plus better robustness at extreme sparsity.

  3. DANCeRS: A Distributed Algorithm for Negotiating Consensus in Robot Swarms with Gaussian Belief Propagation

    cs.RO 2025-08 conditional novelty 6.0 of 10

    A single peer-to-peer message-passing algorithm based on Gaussian Belief Propagation handles both shape-formation consensus and discrete decision consensus, with claimed scalability over prior swarm methods.

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