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arXiv · 2401.16301

Scalable Factor Graph-Based Heterogeneous Bayesian DDF for Dynamic Systems

Ofer Dagan·Tycho L. Cinquini·Nisar R. Ahmed
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Abstract

Heterogeneous Bayesian decentralized data fusion captures the set of problems in which two robots must combine two probability density functions over non-equal, but overlapping sets of random variables. In the context of multi-robot 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 paper presents a framework 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 paper makes contributions by casting the heterogeneous decentralized fusion problem in terms of a factor graph, 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 framework can be used to represent different multi-robot applications and then test it with simulations and hardware experiments to validate and demonstrate its statistical conservativeness, applicability, and robustness to real-world challenges.

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Ofer Dagan, Tycho L. Cinquini, Nisar R. Ahmed. 2024-01-29. Scalable Factor Graph-Based Heterogeneous Bayesian DDF for Dynamic Systems. https://doi.org/10.1109/tro.2025.3637127

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