Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.

翻译：亚符号人工智能方法主导着环境类型分类与同步定位与地图构建领域。然而，这些领域中被忽视的一个重要方面是人机交互空间中的解决方案透明性，因为用于地图生成的亚符号方法并未考虑所生成解决方案的可解释性。本文提出了一种通过符号化同步定位与地图构建（SymboSLAM）进行环境类型分类的创新方法，以弥合可解释性缺口。我们的环境类型分类方法利用本体推理，通过环境中发现的特征综合其上下文。通过向操作者呈现叠加了语义标注的地标与特征占据栅格地图的环境类型分类结果，我们在模型中实现了可解释性。我们使用堪培拉地区的真实地图对SymboSLAM进行评估，证明了该方法的效果。我们通过仿真实验和实际场景测试对系统进行了评估。