  Deploying autonomous robots capable of exploring unknown environments has
long been a topic of great relevance to the robotics community. In this work,
we take a further step in that direction by presenting an open-source active
visual SLAM framework that leverages the accuracy of a state-of-the-art
graph-SLAM system and takes advantage of the fast utility computation that
exploiting the structure of the underlying pose-graph offers. Through careful
estimation of a posteriori weighted pose-graphs, D-optimal decision-making is
achieved online with the objective of improving localization and mapping
uncertainties as exploration occurs.
