mlsanityData to 14 Oct 2026

FutureMapping 2: Gaussian Belief Propagation for Spatial AI

arXiv 1910.14139 ↗First posted 30 Oct 2019cs.AI cs.CV cs.DCcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Oct 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 3; 2024: 2; 2025: 0; 2026: 5.

17 papers.

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