GBP-LOAM: Lidar odometry and mapping using Gaussian Belief Propagation

Ning, Zekun, Pullin, Rhys ORCID: https://orcid.org/0000-0002-2853-6099 and Ji, Ze ORCID: https://orcid.org/0000-0002-8968-9902 2025. GBP-LOAM: Lidar odometry and mapping using Gaussian Belief Propagation. Presented at: The 30th International Conference on Automation and Computing (ICAC 2025), Loughborough, UK, 27-29 August 2025. Item availability restricted.

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Abstract

In Simultaneous Localisation and Mapping (SLAM), factor graphs represent one of the most mature back-end optimisation frameworks. However, most methods are centralised and constrained by computational resources and efficiency. Gaussian Belief Propagation (GBP) is a distributed probabilistic inference method for approximating the probability distribution of a system that can be represented by a Factor Graph. We hypothesise that GBP-based solutions could provide a distributed solution that can efficiently tackle the distributed SLAM challenges. However, there is no existing SLAM framework available based on the GBP solution. As a pilot exploration, this work introduces a new GBP-based 3D SLAM framework, demonstrating the feasibility of GBP-based SLAM in 3D. We construct a factor graph on the back-end of a Lidar Odometry and Mapping (LOAM) pipeline and execute GBP for system optimisation, validating the potential and stability of GBP in SLAM. Our algorithm is evaluated on public datasets and benchmarked against state-of-the-art centralised solvers. The results demonstrate that the GBP-based distributed solver attains accuracy comparable to centralised approaches. However, achieving robust convergence on the SE(3) Lie-group state space remains challenging. Finally, we analyse the convergence of GBP and show that incorporating damping factors and regularisation offers a reliable means to enhance convergence. This work provides valuable insights and foundations for future exploration into multi-robot collaborative SLAM implementations.

Item Type:	Conference or Workshop Item (Paper)
Status:	In Press
Schools:	Schools > Engineering
Date of First Compliant Deposit:	1 August 2025
Last Modified:	01 Aug 2025 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/180199

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