Uncertainty in Artificial Intelligence
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Decentralized Sensor Fusion With Distributed Particle Filters
Matthew Rosencrantz, Geoffrey Gordon, Sebastian Thrun
Abstract:
This paper presents a scalable Bayesian technique for decentralized state estimation from multiple platforms in dynamic environments. As has long been recognized, centralized architectures impose severe scaling limitations for distributed systems due to the enormous communication overheads. We propose a strictly decentralized approach in which only nearby platforms 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 surveillance scenario that arises in a robotic system for playing the game of laser tag. Our results, both from simulation and using physical robots, illustrate an unprecedented scaling capability to large teams of vehicles
Keywords:
Pages: 493-500
PS Link:
PDF Link: /papers/03/p493-rosencrantz.pdf
BibTex:
@INPROCEEDINGS{Rosencrantz03,
AUTHOR = "Matthew Rosencrantz and Geoffrey Gordon and Sebastian Thrun",
TITLE = "Decentralized Sensor Fusion With Distributed Particle Filters",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "493--500"
}


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