Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion
This addresses scalability and modularity challenges for heterogeneous multi-robot systems in real-world missions, though it is incremental as it builds on existing factor graph and decentralized fusion techniques.
The paper tackles the problem of scalable and modular Bayesian decentralized data fusion in heterogeneous multi-robot systems by proposing a factor graph framework that partitions global probability distributions, enabling agents to avoid reasoning over complex models. It validates the method in simulations of multi-target tracking and cooperative mapping, showing gains in computation and communication costs.
This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.