解决视觉惯性定位中超宽带测距的不一致性问题
CVIRO: A Consistent and Tightly-Coupled Visual-Inertial-Ranging Odometry on Lie Groups
- 基于李群构建紧耦合系统，联合估计机器人与信标状态
- 显式建模信标校准不确定性，避免定位性能下降
- 理论证明保持可观测性一致性，适合高精度定位场景
超宽带（UWB）广泛用于缓解视觉惯性里程计（VIO）系统的漂移问题。一致性对确保UWB辅助VIO系统的估计精度至关重要。不一致的估计器会降低定位性能，主要源于两个因素：(1) 估计器未能保持正确的系统可观测性；(2) 假设UWB信标位置已知，导致校准不确定性被不当忽略。本文提出一种基于李群的一致性紧耦合视觉-惯性-测距里程计（CVIRO）系统。该方法将UWB信标状态纳入系统状态，显式考虑校准不确定性，实现机器人与信标状态的联合一致估计。同时，利用李群的不变误差特性，保证可观测性一致性。我们从理论上证明，CVIRO算法自然维持系统的正确不可观测子空间，从而保持估计一致性。大量仿真与实验表明，相较于现有方法，CVIRO在定位精度和一致性方面均有显著提升。
原文摘要 · Abstract (English)
Ultra Wideband (UWB) is widely used to mitigate drift in visual-inertial odometry (VIO) systems. Consistency is crucial for ensuring the estimation accuracy of a UWBaided VIO system. An inconsistent estimator can degrade localization performance, where the inconsistency primarily arises from two main factors: (1) the estimator fails to preserve the correct system observability, and (2) UWB anchor positions are assumed to be known, leading to improper neglect of calibration uncertainty. In this paper, we propose a consistent and tightly-coupled visual-inertial-ranging odometry (CVIRO) system based on the Lie group. Our method incorporates the UWB anchor state into the system state, explicitly accounting for UWB calibration uncertainty and enabling the joint and consistent estimation of both robot and anchor states. Furthermore, observability consistency is ensured by leveraging the invariant error properties of the Lie group. We analytically prove that the CVIRO algorithm naturally maintains the system's correct unobservable subspace, thereby preserving estimation consistency. Extensive simulations and experiments demonstrate that CVIRO achieves superior localization accuracy and consistency compared to existing methods.
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