提出全高斯框架，实现分布式连续时间位姿估计
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
- 用高斯过程+信念传播构建全高斯位姿估计框架
- 支持异步多相机数据，运行效率与现有方法相当
- 适合需要分布式处理的实时视觉系统
连续时间位姿估计算法能有效融合异构传感器并生成平滑轨迹，特别适用于滚动快门摄像头、激光雷达、雷达扫描或事件传感器等非均匀、异步数据流。本文提出G-solver，一种全高斯分布式框架，结合高斯信念传播（GBP）与高斯过程（GP）运动先验，实现连续时间轨迹估计。该方法提供轨迹的概率表示，支持一致插值和数据驱动的超参数优化；同时，GBP具备可扩展的消息传递机制，适合去中心化部署。该框架天然适用于多摄像头场景，无需专门同步或工程适配。在合成数据与真实数据上进行了评估，包括滚动快门和分布式多相机优化任务，结果表明其具有高精度与稳定性，计算耗时与现有连续时间方法相当。开源代码已发布。
原文摘要 · Abstract (English)
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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