🤖 AI Summary
To address observability mismatch and state estimation inconsistency in visual-inertial navigation systems (VINS) caused by linearization-point dependency, this paper proposes a consistency-aware filtering method based on an error-state linear time-varying transformation. The method employs a tightly coupled error-state Kalman filter (ESKF) framework that fuses visual and inertial measurements. Its core contribution lies in constructing a linearization-point-invariant unobservable subspace, thereby preserving the observability structure of the error-state system across arbitrary operating points; additionally, an efficient covariance propagation algorithm is designed to reduce computational overhead. Extensive evaluations on multiple simulated and real-world datasets demonstrate that the proposed approach achieves positioning accuracy comparable to or superior to state-of-the-art VINS methods. The implementation is publicly available as open-source software.
📝 Abstract
This paper presents a novel approach to address the inconsistency problem caused by observability mismatch in visual-inertial navigation systems (VINS). The key idea involves applying a linear time-varying transformation to the error-state within the Error-State Kalman Filter (ESKF). This transformation ensures that extrr{the unobservable subspace of the transformed error-state system} becomes independent of the state, thereby preserving the correct observability of the transformed system against variations in linearization points. We introduce the Transformed ESKF (T-ESKF), a consistent VINS estimator that performs state estimation using the transformed error-state system. Furthermore, we develop an efficient propagation technique to accelerate the covariance propagation based on the transformation relationship between the transition and accumulated matrices of T-ESKF and ESKF. We validate the proposed method through extensive simulations and experiments, demonstrating better (or competitive at least) performance compared to state-of-the-art methods. The code is available at github.com/HITCSC/T-ESKF.