Decentralized Scalar Field Mapping using Gaussian Process
This work addresses scalability and consistency issues in multi-agent scalar-field estimation for applications like environmental monitoring, though it is incremental in its approach to decentralized GP methods.
The paper tackles the problem of inconsistent predictions in decentralized Gaussian process (GP) mapping by multi-agent teams, proposing a data-sharing protocol that improves team-level predictive performance through neighbor-specific assimilation, as demonstrated with positive results.
Decentralized Gaussian process (GP) methods offer a scalable framework for multi-agent scalar-field estimation by replacing a centralized global model with multiple local models maintained by individual agents. A team of agents operates through overlapping domains; neighboring agents generally produce inconsistent distributions over shared regions. This paper investigates whether these inter-agent posterior discrepancies can be systematically exploited to improve team-level predictive performance and answers this question positively through a novel decentralized intersection data-sharing and assimilation protocol. Specifically, each agent constructs neighbor-specific packets from its local GP together with the geometry of the overlap between subdomains and selectively assimilates information received from neighboring agents to improve consistency of its posterior over the shared regions. The proposed architecture preserves locality in both computation and communication, supports decentralized neighbor-to-neighbor data assimilation, and allows local GP models to evolve cooperatively across the network without requiring the exchange full packet exchange or centralized inference.