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.

该论文提出了一种通过符号化同时定位与建图（SymboSLAM）来解决环境类型分类问题的新方法，以弥补人机交互空间中解决方案透明性被忽视的领域。通过对环境中的特征进行本体推理，我们的分类方法能够合成环境的上下文，并通过带有语义标记的地标和特征的占据地图为操作员提供可解释性的模型。我们使用坎培拉地区的真实地图进行了方法有效性的评估，通过模拟和实际试验对系统进行了评估。