In multi-robot applications, inference over large state spaces can often be divided into smaller overlapping subproblems that can then be collaboratively solved in parallel over 'separate' subsets of states. To this end, the factor graph decentralized data fusion (FG-DDF) framework was developed to analyze and exploit conditional independence in heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different locally overlapping 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 empirically 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, and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropout. Simulation and linear 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 95%, thus enabling the design of scalable real-world multi-robot systems.