分布式且一致的多机器人视觉-惯性测距里程计在李群上
Distributed and Consistent Multi-Robot Visual-Inertial-Ranging Odometry on Lie Groups
AI总结：
本文提出DC-VIRO框架，通过融合多机器人视觉-惯性测距数据，在李群上实现鲁棒的分布式定位与自校准。
摘要：
可靠的定位是多机器人系统在GPS拒止环境中运行的基本要求。视觉-惯性里程计（VIO）提供轻量且准确的运动估计，但在没有全局参考的情况下会积累漂移。超宽带（UWB）测距提供互补的全局观测，但大多数现有的UWB辅助VIO方法都是为单机器人场景设计的，并且依赖于预校准的锚点，这限制了其在实际中的鲁棒性。本文提出了一种分布式协作视觉-惯性测距里程计（DC-VIRO）框架，该框架在多个机器人上紧密融合VIO和UWB测量。锚点位置被明确包含在系统状态中以解决校准不确定性，同时通过机器人之间的通信利用共享锚点观测以提供额外的几何约束。通过利用李群上的右不变误差公式，所提出的方法保持了标准VIO的可观测性属性，确保了估计器的一致性。使用多个机器人的仿真结果表明，DC-VIRO显著提高了定位精度和鲁棒性，同时在分布式设置中实现了锚点的自校准。
英文摘要：
Reliable localization is a fundamental requirement for multi-robot systems operating in GPS-denied environments. Visual-inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra-wideband (UWB) ranging offers complementary global observations, yet most existing UWB-aided VIO methods are designed for single-robot scenarios and rely on pre-calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual-inertial-ranging odometry (DC-VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter-robot communication to provide additional geometric constraints. By leveraging a right-invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC-VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self-calibration in distributed settings.
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