A Robot Web for Distributed Many-Device Localisation

Riku Murai1, Joseph Ortiz1, Sajad Saeedi2, Paul H.J. Kelly1, Andrew Davison1
1. Imperial College London, 2. Toronto Metropolitan University


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.



Joining Robot Web

Robot Web is extremely dynamic and allows any-time addition of new robots. The robots have no prior information about their initial poses.

Outlier Measurements and Robust Factors

Robot Web can handle extreme outliers. Here, 30% of the sensor measurements are garbage. Robots automatically reject the garbage measurements (blue lines) and downweights them in the optimisation. Even under this challenging situation, robots are able to localise well.

Relocalisation with Real Robots

We evaluate Robot Web running on real robots. Here, we demonstrate that Robot Web can relocalise the robots even if we pick and place them in a random place.

With landmarks

Without landmarks


    title={A robot web for distributed many-device localisation},
    author={Murai, Riku and Ortiz, Joseph and Saeedi, Sajad and Kelly, Paul HJ and Davison, Andrew J},
    journal={IEEE Transactions on Robotics},


We are grateful to many researchers with whom we have discussed some of the ideas in this paper, especially from the Dyson Robotics Lab and Robot Vision Group at Imperial College, and SLAMcore. We would particularly like to thank Aalok Patwardhan, Marwan Taher, Hussein Ali Jaafar, Stefan Leutenegger, Raluca Scona, Talfan Evans, Eric Dexheimer, Seth Nabarro, Mark van der Wilk, Owen Nicholson, Rob Deaves, Pablo Alcantarilla, Jacek Zienkiewicz, Amanda Prorok, Mac Schwager, Frank Dellaert, Michael Kaess, Tim Barfoot, Richard Newcombe. This work was partially supported by the EPSRC (EP/K008730/1 and EP/P010040/1).