In this article we present a utility function for Active SLAM (A-SLAM) which utilizes map entropy along with D-Optimality criterion metrices for weighting goal frontier candidates. We propose a utility function for frontier goal selection that exploits the occupancy grid map by utilizing the path entropy and favors unknown map locations for maximum area coverage while maintaining a low localization and mapping uncertainties. We quantify the efficiency of our method using various graph connectivity matrices and map efficiency indexes for an environment exploration task. Using simulation and experimental results against similar approaches we achieve an average of 32\% more coverage using publicly available data sets.

翻译：本文提出了一种用于主动SLAM（A-SLAM）的效用函数，该函数利用地图熵以及D-最优性准则度量矩阵对目标前沿候选进行加权。我们提出了一种前沿目标选择的效用函数，通过利用路径熵来挖掘占用栅格地图，并倾向于选择未知地图位置以实现最大面积覆盖，同时保持较低的定位与建图不确定性。我们使用多种图连通性矩阵和地图效率指数，对环境探索任务的算法效率进行了量化。通过模拟与实验对比，在公开数据集上，我们的方法相较于同类方法平均覆盖面积提高了32%。