解决视觉惯性导航中的不一致性问题，提升定位精度与稳定性。
T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation
- 对误差状态施加时变变换，使不可观测子空间与状态解耦。
- 在仿真和实验中表现优于或媲美当前最优方法。
- 适合需要高精度定位的自动驾驶与机器人系统。
本文提出一种新方法，以解决视觉惯性导航系统（VINS）中因可观测性不匹配导致的不一致性问题。核心思想是对接收误差状态的线性时变变换，使变换后误差状态系统的不可观测子空间独立于状态，从而在不同线性化点下保持正确的可观测性。我们提出了变换误差状态卡尔曼滤波器（T-ESKF），一种基于变换误差状态系统的稳健VINS估计器。此外，我们设计了一种高效的传播技术，通过建立T-ESKF与ESKF之间转移矩阵与累积矩阵的变换关系，加速协方差传播。通过大量仿真和实测验证，所提方法在性能上优于或至少媲美现有先进方法。代码已开源：github.com/HITCSC/T-ESKF。
原文摘要 · Abstract (English)
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.
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