T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation

TL;DR

This paper introduces T-ESKF, a transformed error-state Kalman filter that improves the consistency of visual-inertial navigation by preserving correct observability through a novel linear transformation, validated by simulations and experiments.

Contribution

The paper proposes a novel linear time-varying transformation within ESKF to address observability mismatch, enhancing consistency in VINS.

Findings

Improved consistency over existing methods.

Competitive accuracy demonstrated in experiments.

Efficient covariance propagation technique developed.

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 \textrr{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.