论文标题
知道您不知道的内容：使用不可观察的状态应用于Visual惯性大满贯（扩展版）的滑动窗口过滤的一致性（扩展版）
Know What You Don't Know: Consistency in Sliding Window Filtering with Unobservable States Applied to Visual-Inertial SLAM (Extended Version)
论文作者
论文摘要
估计算法（例如滑动窗过滤器）产生所需状态的估计和不确定性。当问题涉及不可观察的状态时，此任务变得具有挑战性。在这些情况下，算法要``知道它不知道的''至关重要，这意味着在算法部署过程中，它必须保持不可观察的状态是不可观察的。这封信提出了在涉及不可观察状态的滑动窗过滤器中保持一致性的一般要求。这些要求对设计导航解决方案的价值在使用IMU预先整合的视觉惯性大满贯的背景下显示。
Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to ``know what it doesn't know'', meaning that it must maintain the unobservable states as unobservable during algorithm deployment. This letter presents general requirements for maintaining consistency in sliding window filters involving unobservable states. The value of these requirements for designing navigation solutions is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.
