In multi-robot applications, inference over large state spaces can often be
divided into smaller overlapping sub-problems 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.Comment: 7 pages, 2 figures, submitted to IEEE Conference on Robotics and
Automation (ICRA 2023