Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM

Abstract:Continuous-time SLAM provides a principled framework for fusing heterogeneous sensors while estimating smooth trajectories, and is particularly well-suited for handling heterogeneous, asynchronous sensor streams with non-uniform readout patterns, such as rolling shutter cameras, LiDAR scanners, radar sweeps, or event-based sensors. In this work, we introduce G-solver, a fully Gaussian and distributed framework that combines Gaussian Belief Propagation (GBP) with Gaussian Process (GP) motion priors for continuous-time trajectory estimation. Our GP model provides a probabilistic representation of the trajectory, enabling consistent interpolation and the use of data-driven hyperparameters, while GBP offers a scalable message-passing formulation well-suited for decentralized settings. The resulting solver naturally extends to multi-camera scenarios without specialized synchronization or engineering effort. We evaluate the approach on synthetic and real data, including rolling shutter and distributed multi-camera optimization, demonstrating accurate and stable estimation with runtimes comparable to existing continuous-time methods. An open-source implementation is released.

To be published in RA-L. Open-source implementation is released at this https URL

Robotics (cs.RO)

arXiv:2606.06250 [cs.RO]

(or arXiv:2606.06250v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.06250 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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