{"ID":3083678,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T07:23:37.79250861Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06250","arxiv_id":"2606.06250","title":"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.","short_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-b...","url_abs":"https://arxiv.org/abs/2606.06250","url_pdf":"https://arxiv.org/pdf/2606.06250v1","authors":"[\"Davide Ceriola\",\"Simone Ferrari\",\"Luca Di Giammarino\",\"Leonardo Brizi\",\"Giorgio Grisetti\"]","published":"2026-06-04T14:52:25Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}