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.

通过机器人之间的 peer-to-peer 通信，在使用高斯信念传播在非线性因子图上对机器人网络观测的概率结构进行计算的基础上，我们实现了一个灵活的 Robot Web 解决方案，可以解决千个机器人相互交互的问题，即使测量传感器或通信数据包出错，该方法也具有容错性。