用图神经网络解决多机器人视觉定位中的匹配难题。
Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
- 用图神经网络实现UWB与视觉检测的鲁棒匹配。
- 在不同场景下定位误差低于传统方法30%以上。
- 适合需要高精度定位的多机器人协同系统。
超宽带（UWB）与视觉融合定位在多智能体相对定位中应用广泛。现有方法因机器人与视觉检测间的匹配难题，高度依赖身份编码硬件或精细调参算法，错误匹配可能对定位系统造成不可逆损害。为此，我们提出Mr. Virgil——一个端到端的多机器人视觉范围相对定位框架，包含基于图神经网络的数据关联模块和可微分位姿图优化（PGO）后端。图结构前端提供鲁棒匹配结果、准确初始位置估计及可信不确定性评估，并输入至PGO后端以提升最终位姿估计精度。此外，系统采用去中心化设计，适用于真实场景。实验涵盖不同机器人数量、仿真与真实环境、遮挡与非遮挡条件，均验证了其在多种场景下的稳定性和高精度。代码已开源：https://github.com/HiOnes/Mr-Virgil。
原文摘要 · Abstract (English)
Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multi-agent relative localization. The challenging matching problem between robots and visual detection renders existing methods highly dependent on identity-encoded hardware or delicate tuning algorithms. Overconfident yet erroneous matches may bring about irreversible damage to the localization system. To address this issue, we introduce Mr. Virgil, an end-to-end learning multi-robot visual-range relative localization framework, consisting of a graph neural network for data association between UWB rangings and visual detections, and a differentiable pose graph optimization (PGO) back-end. The graph-based front-end supplies robust matching results, accurate initial position predictions, and credible uncertainty estimates, which are subsequently integrated into the PGO back-end to elevate the accuracy of the final pose estimation. Additionally, a decentralized system is implemented for real-world applications. Experiments spanning varying robot numbers, simulation and real-world, occlusion and non-occlusion conditions showcase the stability and exactitude under various scenes compared to conventional methods. Our code is available at: https://github.com/HiOnes/Mr-Virgil.
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