We present a novel scalable, fully distributed, and online method for simultaneous localisation and extrinsic calibration for multi-robot setups. Individual a priori unknown robot poses are probabilistically inferred as robots sense each other while simultaneously calibrating their sensors and markers extrinsic using Gaussian Belief Propagation. In the presented experiments, we show how our method not only yields accurate robot localisation and auto-calibration but also is able to perform under challenging circumstances such as highly noisy measurements, significant communication failures or limited communication range.

翻译：我们提出了一种新颖的、可扩展的、完全分布式且在线的方法，用于多机器人系统中的同步定位与外部参数标定。当机器人相互感知时，单个先验未知的机器人位姿通过概率推断得以实现，同时利用高斯置信传播对其传感器与标记物的外部参数进行自动标定。在实验展示中，我们证明该方法不仅能实现精确的机器人定位与自动标定，还能在诸如强噪声测量、严重通信故障或有限通信范围等挑战性条件下有效运作。