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Scalable Factor Graph-Based Heterogeneous Bayesian DDF for Dynamic Systems
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AI
DOI：10.1109/TRO.2025.3637127.png)
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Abstract
En 中文
Heterogeneous Bayesian decentralized data fusion captures the set of problems in which two or more robots must combine probability density functions over nonequal, but overlapping sets of random variables. In the context of multirobot dynamic systems, this enables robots to take a “divide and conquer” approach to reason and share data over complementary tasks instead of over the full joint state space. For example, in a target tracking application, this allows robots to track different subsets of targets and share data on only common targets. This article presents a system by which robots can each use a local factor graph 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 costs. From a theoretical point of view, this article makes contributions by casting the heterogeneous decentralized fusion problem in terms of factor graphs, analyzing the challenges that arise due to dynamic filtering, and then developing a new conservative filtering algorithm that ensures statistical correctness. From a practical point of view, we show how this system can be used to represent different multirobot applications and then test it with simulations and hardware experiments to validate and demonstrate its statistical conservativeness, applicability, and robustness to real-world challenges.
Keywords:
Bayesian decentralized data fusion (DDF)
factor graphs
heterogeneous multirobot systems
sensor fusion
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