Decentralized Sensor Fusion With Distributed Particle Filters

TL;DR

This paper introduces a scalable decentralized Bayesian method using distributed particle filters for multi-platform state estimation, significantly reducing communication overhead and enabling large robotic teams to operate efficiently.

Contribution

It proposes a novel decentralized approach with interactive communication for distributed particle filtering, overcoming scaling limitations of centralized systems.

Findings

Demonstrates effective decentralized state estimation in simulated environments.

Validates scalability with physical robot experiments.

Achieves efficient information exchange among large vehicle teams.

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.