FUSE：一种用于车辆和机器人SLAM系统统一状态估计的框架
FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems
AI总结：
本文提出FUSE框架，用于统一车辆和机器人SLAM系统中的状态估计，通过分离时间处理、局部几何关联、估计器公式和地图更新策略，提高状态估计设计的灵活性和准确性。
摘要：
在混合速率传感下，紧密耦合的SLAM公式通常将时间处理、局部几何关联、估计器公式和地图更新策略绑定到特定方法的设计中。这种绑定使得难以在不重新设计其余状态估计过程的情况下改变一个设计选择。本文提出了FUSE，一种用于车辆和机器人SLAM系统统一状态估计的框架。FUSE围绕观察摄入、传播、更新和状态查询组织状态估计接口，并利用此接口将时间处理、残差准备的局部几何关联、估计器公式和地图更新策略分开。开发了一个LiDAR-IMU实例来在混合速率传感和方向退化下检验该框架，其中高速惯性传播、LiDAR触发的几何更新、残差筛选和退化感知的修正通过相同的接口边界操作。在418米的环形走廊序列中，该实例报告了1.626米的端到端轨迹误差，与Faster-LIO相比，相对误差减少了7.9%。结果支持FUSE作为组织状态估计设计选择的框架，并展示了评估实例如何在弱可观测方向上正则化更新。
英文摘要：
Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-specific designs. Such binding makes it difficult to vary one design choice without re-engineering the rest of the state-estimation process. This paper presents FUSE, a framework for unified state estimation in vehicular and robotic SLAM systems. FUSE organizes the state-estimation interface around observation ingestion, propagation, update, and state query, and uses this interface to separate temporal processing, residual-ready local geometric association, estimator formulation, and map-update policy. A LiDAR--IMU instantiation is developed to examine the framework under mixed-rate sensing and directional degeneracy, where high-rate inertial propagation, LiDAR-triggered geometric update, residual screening, and degeneracy-aware correction operate through the same interface boundaries. On a 418~m loop-corridor sequence, the instantiation reports a 1.626 m end-to-end trajectory error, corresponding to a 7.9% relative error reduction compared with Faster-LIO, the lowest-error baseline on this sequence. The results support FUSE as a framework for organizing state-estimation design choices and show how the evaluated instantiation regularizes updates along weakly observable directions.
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