We show that a distributed network of robots or other devices which make measurements of each other can collaborate to globally localise via efficient ad-hoc peer to peer communication. Our Robot Web solution is based on Gaussian Belief Propagation on the fundamental non-linear factor graph describing the probabilistic structure of all of the observations robots make internally or of each other, and is flexible for any type of robot, motion or sensor. We define a simple and efficient communication protocol which can be implemented by the publishing and reading of web pages or other asynchronous communication technologies. We show in simulations with up to 1000 robots interacting in arbitrary patterns that our solution convergently achieves global accuracy as accurate as a centralised non-linear factor graph solver while operating with high distributed efficiency of computation and communication. Via the use of robust factors in GBP, our method is tolerant to a high percentage of faults in sensor measurements or dropped communication packets.

翻译：我们证明，由机器人或其他设备构成的分布式网络通过相互测量，能够通过高效的即兴点对点通信实现全局定位。我们的机器人网络解决方案基于高斯置信传播，其核心是非线性因子图，描述了所有机器人内部观测或相互观测的概率结构，适用于任意类型的机器人、运动或传感器。我们定义了一种简单高效的通信协议，可通过网页的发布与读取或其他异步通信技术实现。在多达1000个机器人以任意模式交互的仿真中，我们的方案收敛地达到了与集中式非线性因子图求解器相当的全局精度，同时保持计算与通信的高度分布式效率。通过在高斯置信传播中使用鲁棒因子，我们的方法能够容忍传感器测量或通信数据包丢失中的高比例故障。