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 when designing a navigation solution is experimentally shown within the context of visual-inertial SLAM making use of IMU preintegration.

翻译：估计算法（如滑动窗口滤波器）会生成所需状态的估计值及不确定度。当问题涉及不可观状态时，这一任务变得具有挑战性。在此类情况下，算法必须“知道其所不知道的”，即在算法部署过程中保持不可观状态的不可观性。本文提出了在涉及不可观状态的滑动窗口滤波器中维持一致性的通用要求。通过利用IMU预积分的视觉-惯性SLAM实验，展示了这些要求在设计导航方案中的价值。