Scalable Factor Graph-Based Heterogeneous Bayesian DDF for Dynamic Systems
CoRR(2024)
摘要
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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