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：本文提出了一种面向主动SLAM（A-SLAM）的效用函数，该函数利用地图熵与D-最优性准则度量指标对目标前沿候选点进行加权。我们设计了一种前沿目标选择效用函数，通过利用占用栅格地图中的路径熵，在保持较低定位与建图不确定性的前提下，优先选择未知地图区域以实现最大面积覆盖。我们采用多种图连通性矩阵与地图效率指标，对环境探索任务中本方法的效率进行量化评估。通过仿真与实验结果的对比分析，基于公开数据集验证表明，相较于同类方法，本方法平均覆盖率提升了32%。