In this work, we fully define the existing relationships between traditional optimality criteria and the connectivity of the underlying pose-graph in Active SLAM, characterizing, therefore, the connection between Graph Theory and the Theory Optimal Experimental Design. We validate the proposed relationships in 2D and 3D graph SLAM datasets, showing a remarkable relaxation of the computational load when using the graph structure. Furthermore, we present a novel Active SLAM framework which outperforms traditional methods by successfully leveraging the graphical facet of the problem so as to autonomously explore an unknown environment.

翻译：在这项工作中,我们充分界定了传统最佳性标准与主动SLM中基本面貌图的连通性之间的现有关系,因此说明了图形理论与理论最佳实验设计之间的联系。我们验证了2D和3D图SLM数据集中的拟议关系,表明在使用图形结构时计算负荷明显放松。此外,我们提出了一个新型的主动SLM框架,它通过成功地利用问题的图形面来自动探索未知的环境,优于传统方法。