  Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the
Robotics community to be a mature field. Currently, there are many open-source
systems that are able to deliver fast and accurate estimation in typical
real-world scenarios. Still, all these systems often provide an ad-hoc
implementation that entailed to predefined sensor configurations. In this work,
we tackle this issue, proposing a novel SLAM architecture specifically designed
to address heterogeneous sensors' configuration and to standardize SLAM
solutions. Thanks to its modularity and to specific design patterns, the
presented architecture is easy to extend, enhancing code reuse and efficiency.
Finally, adopting our solution, we conducted comparative experiments for a
variety of sensor configurations, showing competitive results that confirm
state-of-the-art performance.
