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

TL;DR

This paper introduces ExplORB-SLAM, an open-source active visual SLAM framework that enhances exploration efficiency by leveraging pose-graph topology for improved decision-making and uncertainty reduction.

Contribution

It presents a novel active visual SLAM approach that exploits pose-graph structure for real-time decision-making, improving exploration and mapping accuracy.

Findings

Achieves online D-optimal decision-making during exploration.

Improves localization and mapping uncertainties.

Leverages pose-graph topology for efficient utility computation.

Abstract

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.