Gaussian Belief Propagation

Latest papers

10 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery
Oct 4, 2026Yuzhou Cheng, Tom Yates, Ignacio Alzugaray +3Gaussian Belief Propagation Belief Propagation
Imperial College London, London, United Kingdom.
Belief-Aware Multi-Agent Path Finding under Map Uncertainty
Sep 30, 2026Viraj Parimi, Shao-Hung Chan, Han Zhang +2Belief-Space Planning Gaussian Belief Propagation
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 01239. · Symbotic Inc., Wilmington, MA 01887, USA
Collective Ranking of Environmental Signals through Gaussian Belief Propagation in a Patrolling Robot Swarm
Aug 18, 2026Zachary R. Madin, Connor York, Jonathan Lawry +1Gaussian Belief Propagation Multi-Robot Systems
University of Bristol, Bristol, UK
Normalizing Flow-Enhanced Message Passing for Multirobot Collaborative Localization
Jun 29, 2026Han Shen, Guanghui Wen, Liangming Chen +1Bayesian Inference Cooperative Localization
Department of Systems Science, Southeast University, Nanjing 211189, China · School of Automation, Southeast University, Nanjing 210096, China · School of Automation and Intelligent Manufacturing and Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen 518055, China +1
Equivariant Neural Belief Propagation
Jun 4, 2026Zehua Cheng, Wei Dai, Jiahao SunEquivariant Representation Learning Factor Graph Optimization
Department of Computer Science, University of Oxford, Oxford, United Kingdom · FLock.io, London, United Kingdom
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
Jun 4, 2026Davide Ceriola, Simone Ferrari, Luca Di Giammarino +2Simultaneous Localization and Mapping Bayesian Inference
Department of Computer, Control, and Management Engineering “Antonio Ruberti”, Sapienza University of Rome, Italy · University of Stuttgart, Germany
PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation
Jun 2, 2026Shinjeong Kim, Ignacio Alzugaray, Callum Rhodes +2Depth Estimation Visual Odometry
Department of Computing, Imperial College London
A Distributed Gaussian Process Model for Multi-Robot Mapping
Mar 7, 2026Seth Nabarro, Mark van der Wilk, Andrew J. DavisonSparse Gaussian Processes Gaussian Belief Propagation
Dyson Robotics Lab, Imperial College London · Department of Computer Science, University of Oxford
FastBUS: A Fast Bayesian Framework for Unified Weakly-Supervised Learning
Feb 28, 2026Ziquan Wang, Haobo Wang, Ke Chen +2Gaussian Belief Propagation Weakly Supervised Learning
the School of Software Technology, Zhejiang University, Hangzhou 310027, China · the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Zhejiang 310027, China, and also with the Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Binjiang 310000, China · the School of Computer Science and Engineering, Southeast University, Nanjing 210096, China +1
Gaussian Belief Propagation Network for Depth Completion
Jan 29, 2026Jie Tang, Pingping Xie, Jian Li +1Markov Random Fields Depth Estimation
National University of Defense Technology, Changsha, China · Hong Kong University of Science and Technology, Hong Kong, China

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Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery

Yuzhou Cheng, Tom Yates, Ignacio Alzugaray, Danyal Akarca+2

Imperial College London, London, United Kingdom.

Gaussian Belief Propagation Belief Propagation Factor Graph Optimization Probabilistic Graphical Models Bayesian Inference Pose Graph Optimization

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Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spatial intelligence. However, we find GBP most effective locally: it rapidly smooths message errors that vary sharply between neighbor variables, but corrects global errors across distant graph regions incrementally through long-range message propagations. We propose Hierarchy-GBP (H-GBP), an iterative, two-stage framework that accelerates GBP by first solving these global errors with a coarse graph approximation (abstraction) and projecting the results back to the original graph (recovery), then refining the remaining local errors with GBP. We prove H-GBP convergence to the optimum by deriving the combined matrix operator of our abstraction and recovery steps and analyzing its spectral radius. Experiments on linear sparse graphs show that H-GBP converges fundamentally faster than standard GBP. Moreover, we validate H-GBP on two important spatial problems: Pose Graph Optimization (PGO) and Bundle Adjustment (BA). H-GBP markedly accelerates large-scale PGO and achieves state-of-the-art runtime across all tested BA scales.