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Deploying autonomous robots capable of exploring unknown environments haslong 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 activevisual SLAM framework that leverages the accuracy of a state-of-the-artgraph-SLAM system and takes advantage of the fast utility computation thatexploiting the structure of the underlying pose-graph offers. Through carefulestimation of a posteriori weighted pose-graphs, D-optimal decision-making isachieved online with the objective of improving localization and mappinguncertainties as exploration occurs.

ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology