  This paper presents a method for Bayesian multi-robot peer-to-peer data
fusion where any pair of autonomous robots hold non-identical, but overlapping
parts of a global joint probability distribution, representing real world
inference tasks (e.g., mapping, tracking). It is shown that in dynamic
stochastic systems, filtering, which corresponds to marginalization of past
variables, results in direct and hidden dependencies between variables not
mutually monitored by the robots, which might lead to an overconfident fused
estimate. The paper makes both theoretical and practical contributions by
providing (i) a rigorous analysis of the origin of the dependencies and and
(ii) a conservative filtering algorithm for heterogeneous data fusion in
dynamic systems that can be integrated with existing fusion algorithms. This
work uses factor graphs as an analysis tool and an inference engine. Each robot
in the network maintains a local factor graph and communicates only relevant
parts of it (a sub-graph) to its neighboring robot. We discuss the
applicability to various multi-robot robotic applications and demonstrate the
performance using a multi-robot multi-target tracking simulation, showing that
the proposed algorithm produces conservative estimates at each robot.
