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.

翻译：长期以来,部署能够探索未知环境的自主机器人一直是一个与机器人界密切相关的主题。在这项工作中,我们在这方面又迈出了一步,提出一个开放源代码主动直观的SLAM框架,利用最先进的图形-SLAM系统的准确性,并利用利用基本地形图结构的快速实用计算方法。通过仔细估计事后加权面貌,在网上实现最佳决策,目的是随着勘探的进行改善本地化和绘制不确定性图。