The main contribution of this paper is an invariant extended Kalman filter
(EKF) for visual inertial navigation systems (VINS). It is demonstrated that
the conventional EKF based VINS is not invariant under the stochastic
unobservable transformation, associated with translations and a rotation about
the gravitational direction. This can lead to inconsistent state estimates as
the estimator does not obey a fundamental property of the physical system. To
address this issue, we use a novel uncertainty representation to derive a Right
Invariant error extended Kalman filter (RIEKF-VINS) that preserves this
invariance property. RIEKF-VINS is then adapted to the multistate constraint
Kalman filter framework to obtain a consistent state estimator. Both Monte
Carlo simulations and real-world experiments are used to validate the proposed
method.

本文提出了一种基于不变扩展卡尔曼滤波器 (RIEKF-VINS) 的方法，并通过 Monte Carlo 模拟和实际实验验证了该方法在视觉惯性导航系统中的有效性。该方法使用一种新的不确定性表示方法，可以保留 VINS 中的不变性质，并将 RIEKF-VINS 适用于多状态约束卡尔曼滤波器框架，从而获得一致性的状态估计。