This paper presents a scalable Bayesian tech nique for decentralized state estimation from multiple platforms in dynamic environments. As has long been recognized, centralized architec tures impose severe scaling limitations for dis tributed systems due to the enormous commu nication overheads. We propose a strictly de centralized approach in which only nearby plat forms exchange information. They do so through an interactive communication protocol aimed at maximizing information flow. Our approach is evaluated in the context of a distributed surveil lance scenario that arises in a robotic system for playing the game of laser tag. Our results, both from simulation and using physical robots, illus trate an unprecedented scaling capability to large teams of vehicles.
Rosencrantz et al. REF proposed a strictly decentralized approach in which only nearby platforms exchange information to maximize the information flow and evaluated it in a robotic systems for playing the game of laser tag.
Decentralized Sensor Fusion With Distributed Particle Filters
ArXiv
ArXiv
Computer Science