Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception

  • Ruofei Ouyang
  • Bryan Kian Hsiang LowEmail author
Part of the following topical collections:
  1. Special Issue on Multi-Robot and Multi-Agent Systems


This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of large-scale environmental phenomena. To overcome the limitations of scale in existing works, our proposed algorithms allow every mobile sensing agent to utilize a different support set and dynamically switch to another during execution for encapsulating its own data into a local summary that, perhaps surprisingly, can still be assimilated with the other agents’ local summaries (i.e., based on their current support sets) into a globally consistent summary to be used for predicting the phenomenon. To achieve this, we propose a novel transfer learning mechanism for a team of agents capable of sharing and transferring information encapsulated in a summary based on a support set to that utilizing a different support set with some loss that can be theoretically bounded and analyzed. To alleviate the issue of information loss accumulating over multiple instances of transfer learning, we propose a new information sharing mechanism to be incorporated into our algorithms in order to achieve memory-efficient lazy transfer learning. Empirical evaluation on three real-world datasets for up to 128 agents show that our algorithms outperform the state-of-the-art methods.


Gaussian process Decentralized data fusion Scalability 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceNational University of SingaporeSingaporeRepublic of Singapore

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