An Invariant-EKF VINS Algorithm for Improving Consistency
This work addresses inconsistency issues in VINS for robotics and autonomous systems, representing an incremental improvement over existing methods.
The paper tackled inconsistency in visual inertial navigation systems (VINS) by developing an invariant extended Kalman filter (RIEKF-VINS) that preserves invariance under stochastic unobservable transformations, resulting in improved state estimation consistency validated through simulations and real-world experiments.
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.