  In many robotics problems, there is a significant gain in collaborative
information sharing between multiple robots, for exploration, search and
rescue, tracking multiple targets, or mapping large environments. One of the
key implicit assumptions when solving cooperative multi-robot problems is that
all robots use the same (homogeneous) underlying algorithm. However, in
practice, we want to allow collaboration between robots possessing different
capabilities and that therefore must rely on heterogeneous algorithms. We
present a system architecture and the supporting theory, to enable
collaboration in a decentralized network of robots, where each robot relies on
different estimation algorithms. To develop our approach, we focus on
multi-robot simultaneous localization and mapping (SLAM) with multi-target
tracking. Our theoretical framework builds on our idea of exploiting the
conditional independence structure inherent to many robotics applications to
separate between each robot's local inference (estimation) tasks and fuse only
relevant parts of their non-equal, but overlapping probability density function
(pdfs). We present a new decentralized graph-based approach to the multi-robot
SLAM and tracking problem. We leverage factor graphs to split between different
parts of the problem for efficient data sharing between robots in the network
while enabling robots to use different local sparse
landmark/dense/metric-semantic SLAM algorithms.
