AI 中文总结
针对经典编队控制问题，提出基于图神经网络的仅视觉去中心化相对姿态估计框架，通过隐式虚拟领导者解决局限性，附加相关模块进行不确定性处理，经多测试集验证，该框架姿态估计精度高且能推广到不同平台和编队规模。
AI 中文摘要
经典的领导者-跟随者编队控制存在单点故障和误差传播问题，且依赖于在GPS受限环境中不适用的绝对定位传感器。我们通过引入基于图神经网络（GNN）的完全去中心化、仅视觉的相对姿态估计框架来解决这些限制。关键思想是隐式虚拟领导者（IVL）：一个不与任何单个机器人绑定但在GNN中仅使用单目图像和机器人间通信隐式学习的非物理编队参考框架。我们附加了一个用于偶然不确定性的异方差GNLL头和用于认知不确定性的MC Dropout，并在模拟和真实世界测试集上进行了系统比较。我们的框架实现了有竞争力的姿态估计精度，并自然地推广到异构机器人平台和不同的编队规模。
英文摘要
Classical leader-follower formation control suffers from single points of failure and error propagation, and relies on absolute localization sensors that are ill-suited for GPS-denied environments. We present a learned, vision-only estimator that maps each robot's monocular image, together with messages exchanged over a communication graph, directly to its 6-DoF relative pose. Its key ingredient is the implicit virtual leader (IVL): a non-physical reference frame at the team centroid, implicitly learned inside a Transformer-based graph neural network, so that estimation has no privileged node and needs no absolute localization. The estimator additionally reports well-calibrated aleatoric (heteroscedastic GNLL) uncertainty alongside epistemic (MC~Dropout) uncertainty, compared systematically across simulation and real-world test sets. Trained only in simulation, the estimator generalizes to unseen scenes, to larger unseen team sizes, and to an external real-world benchmark. It exhibits no single point of failure: removing any one robot costs at most $1.24\times$ the median removal, and removing $71\%$ of the communication links costs $1.77\times$ in position error without retraining. Trained on real-robot data from a single platform, it transfers without modification to a heterogeneous team, estimating relative pose to $0.22$\,m and $1.6^\circ$ on physical robots, where it drives closed-loop formation control.
