打破时间：一种用于分布式和连续时间SLAM的全高斯框架
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
AI总结：
提出G-solver，结合高斯信念传播和高斯过程运动先验的分布式连续时间轨迹估计框架，支持异构异步传感器和多相机场景。
摘要：
连续时间SLAM为融合异构传感器同时估计平滑轨迹提供了原则性框架，特别适合处理具有非均匀读出模式的异构、异步传感器流，如卷帘快门相机、激光雷达扫描仪、雷达扫描或事件相机。在这项工作中，我们引入了G-solver，一个全高斯分布式框架，将高斯信念传播（GBP）与高斯过程（GP）运动先验相结合，用于连续时间轨迹估计。我们的GP模型提供了轨迹的概率表示，支持一致插值和数据驱动超参数的使用，而GBP提供了一种适用于分散设置的可扩展消息传递公式。由此产生的求解器自然地扩展到多相机场景，无需专门的同步或工程工作。我们在合成数据和真实数据上评估了该方法，包括卷帘快门和分布式多相机优化，展示了与现有连续时间方法相当的运行时间下的准确稳定估计。发布了开源实现。
英文摘要：
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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