# FutureMapping 2: Gaussian Belief Propagation for Spatial AI

arXiv: https://arxiv.org/abs/1910.14139 · HTML: https://mlsanity.com/paper/1910.14139/

## Key facts

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

## Built on by (most cited first, 17 of 17)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [A visual introduction to Gaussian Belief Propagation](https://mlsanity.com/paper/2107.02308/) (2021)
- [A Robot Web for Distributed Many-Device Localisation](https://mlsanity.com/paper/2202.03314/) (2022)
- [TextSLAM: Visual SLAM with Semantic Planar Text Features](https://mlsanity.com/paper/2305.10029/) (2023)
- [RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization](https://mlsanity.com/paper/2202.13221/) (2022)
- [Distributing Collaborative Multi-Robot Planning with Gaussian Belief Propagation](https://mlsanity.com/paper/2203.11618/) (2022)
- [Incremental Abstraction in Distributed Probabilistic SLAM Graphs](https://mlsanity.com/paper/2109.06241/) (2021)
- [Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems](https://mlsanity.com/paper/2402.08193/) (2024)
- [Learning in Deep Factor Graphs with Gaussian Belief Propagation](https://mlsanity.com/paper/2311.14649/) (2023)
- [Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM](https://mlsanity.com/paper/2407.07074/) (2024)
- [Gaussian Belief Propagation Network for Depth Completion](https://mlsanity.com/paper/2601.21291/) (2026)
- [Visual General Intelligence: A White Paper](https://mlsanity.com/paper/2608.25924/) (2026)
- [A Distributed Gaussian Process Model for Multi-Robot Mapping](https://mlsanity.com/paper/2603.07351/) (2026)
- [PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation](https://mlsanity.com/paper/2606.03989/) (2026)
- [Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery](https://mlsanity.com/paper/2610.06978/) (2026)

## 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.