论文标题
建立一致，高效的基于地图的视觉惯性本地化：理论框架和滤波器设计
Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design
论文作者
论文摘要
本文着重于设计一个一致，有效的过滤器，以用于基于地图的视觉惯性定位。首先，我们提出了一个具有其代数的新谎言组，以此为基础，设计了一个新颖的扩展Kalman滤波器（不变的EKF）。从理论上讲，我们证明，当我们不考虑地图信息的不确定性时，提出的不变EKF自然可以保持系统的正确可观察性属性。为了考虑地图信息的不确定性，我们引入了施密特过滤器。使用Schmidt滤波器，可以考虑地图信息的不确定性，以避免过度信心估计，而计算成本仅随着MAP KEYFRAMES的大小而线性增加。此外，我们引入了一种易于实现的可观察性约束技术，因为将不变的EKF与Schmidt滤波器直接相结合，无法维护系统的正确可观察性属性，以考虑映射信息的不确定性。最后，我们通过广泛的模拟和现实世界实验来验证我们提出的系统的高一致性，准确性和效率。
This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of the map information, the proposed invariant EKF can naturally maintain the correct observability properties of the system. To consider the uncertainty of the map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of the map information can be taken into consideration to avoid over-confident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of the map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.
