Scalable Factor Graph-Based Heterogeneous Bayesian DDF for Dynamic Systems
| Autor: | Dagan, Ofer, Cinquini, Tycho L., Ahmed, Nisar R. |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 15 pages, 13 figures, submitted for review at IEEE Transactions on Robotics (T-RO) |
| Databáze: | arXiv |
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