  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.
