  The factor graph decentralized data fusion (FG-DDF) framework was developed
for the analysis and exploitation of conditional independence in {heterogeneous
Bayesian decentralized fusion problems, in which robots update and fuse pdfs
over different, but overlapping subsets of random states. This allows robots to
efficiently use smaller probabilistic models and sparse message passing to
accurately and scalably fuse relevant local parts of a larger global joint
state pdf while accounting for data dependencies between robots. Whereas prior
work required limiting assumptions about network connectivity and model
linearity, this paper relaxes these to explore the applicability and robustness
of FG-DDF in more general settings. We develop a new heterogeneous fusion rule
which generalizes the homogeneous covariance intersection algorithm for such
cases and test it in multi-robot tracking and localization scenarios with
non-linear motion/observation models under communication dropouts. Simulation
and hardware experiments show that, in practice, the FG-DDF continues to
provide consistent filtered estimates under these more practical operating
conditions, while reducing computation and communication costs by more than
99\%, thus enabling the design of scalable real-world multi-robot systems.
