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.

翻译：可靠的定位是多机器人系统在无GPS环境中运行的基本要求。视觉惯性里程计（VIO）提供了轻量且精确的运动估计，但在缺乏全局参考的情况下会存在累积漂移。超宽带（UWB）测距提供了互补的全局观测，然而大多数现有的UWB辅助VIO方法是为单机器人场景设计的，并且依赖于预标定的锚点，这限制了其在实践中的鲁棒性。本文提出了一种分布式协同视觉-惯性-测距里程计（DC-VIRO）框架，该框架紧密融合了多个机器人间的VIO和UWB测量值。系统状态中显式包含了锚点位置以解决标定不确定性，同时通过机器人间通信利用共享的锚点观测来提供额外的几何约束。通过在李群上利用右不变误差公式，所提出的方法保留了标准VIO的可观测性特性，确保了估计器的一致性。多机器人仿真结果表明，DC-VIRO显著提高了定位精度和鲁棒性，同时实现了分布式设置下的锚点自标定。