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, 如滑动窗口过滤器, 产生对理想状态的估计和不确定性。 当问题涉及到不可观察状态时, 这项任务就变得具有挑战性。 在这些情况下, 算法必须“ 知道它不知道什么 ”, 意思是它必须在算法部署期间将不可观察状态维持为不可观察状态。 本信提出了在涉及不可观察状态的滑动窗口过滤器中保持一致性的一般要求。 这些要求在设计导航解决方案时的价值在视觉- 罪恶的 SLM 范围内实验显示, 使用IMU 预整合。