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

• URL: http://arxiv.org/abs/2606.06250v1
• Date: Thu, 04 Jun 2026 14:52:25 GMT
• Title: Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
• Abstract summary: We introduce G-solver, a fully Gaussian and distributed framework for continuous-time trajectory estimation.<n>We evaluate the approach on synthetic and real data, including rolling shutter and distributed multi-camera optimization.
• Score: 3.377467347364114
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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