MSCEqF：一种用于视觉辅助惯性导航的多状态约束等变滤波器
MSCEqF: A Multi State Constraint Equivariant Filter for Vision-aided Inertial Navigation
AI总结：
针对VINS问题提出多状态约束等变滤波器MSCEqF，通过定义包含IMU偏置和相机标定的对称群结构，解决了现有IEKF方法状态附加问题，在显著误差下保持内在一致性，显著提升了估计性能。
摘要：
本信重新审视了视觉惯性导航系统（VINS）问题，并提出了一种新型滤波器设计，我们称之为多状态约束等变滤波器（MSCEqF，类比著名的MSCKF）。我们定义了一个对称群及相应的群作用，专门用于设计包含IMU偏置、相机内参和外参标定状态的视觉惯性里程计（VIO）等变滤波器。现有最先进的不变扩展卡尔曼滤波器（IEKF）方法仅将IMU偏置和其他状态简单附加到 $\mathbf{SE}_2(3)$ 群上，与之不同的是，我们的滤波器建立在对称性基础上，将所有状态恰当地包含在群结构中。因此，我们实现了改进的性能，特别是在线性化点大幅偏离真值时（即在状态扰动引起的瞬态期间）。我们的方法在显著误差收敛阶段也具有内在的一致性，无需误差不确定性自适应、可观性约束或其他强制一致性技术。这极大地改善了估计器在显著误差和意外状态变化（例如在长时间任务中）下的行为。我们使用三个不同的著名真实世界数据集进行了大量不同的实验来评估该方法。
英文摘要：
This letter re-visits the problem of visual-inertial navigation system (VINS) and presents a novel filter design we dub the multi state constraint equivariant filter (MSCEqF, in analogy to the well known MSCKF). We define a symmetry group and corresponding group action that allow specifically the design of an equivariant filter for the problem of visual-inertial odometry (VIO) including IMU bias, and camera intrinsic and extrinsic calibration states. In contrast to state-of-the-art invariant extended Kalman filter (IEKF) approaches that simply tack IMU bias and other states onto the $\mathbf{SE}_2(3)$ group, our filter builds upon a symmetry that properly includes all the states in the group structure. Thus, we achieve improved behavior, particularly when linearization points largely deviate from the truth (i.e., on transients upon state disturbances). Our approach is inherently consistent even during convergence phases from significant errors without the need for error uncertainty adaptation, observability constraint, or other consistency enforcing techniques. This leads to greatly improved estimator behavior for significant error and unexpected state changes during, e.g., long-duration missions. We evaluate our approach with a multitude of different experiments using three different prominent real-world datasets.
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