Abstract
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.
Abstract (translated)
子符号人工智能方法在环境类型分类和同时定位与映射领域占据主导地位。然而，在这些领域中，一个被忽视的领域是解决人机交互空间的可解释性，因为用于地图生成的子符号方法没有解释生成的解决方案。本文提出了一种名为符号同时定位与映射的新方法，通过Symbolic Simultaneous Localisation and Mapping (SymboSLAM)，桥接了可解释性差距。我们的环境类型分类方法通过观察发现环境通过特征内的上下文合成。通过在模型中呈现带有环境类型分类的运营商，我们实现了模型的可解释性。我们在堪培拉地区的地面真值地图上评估了SymboSLAM，证明了该方法的有效性。我们通过仿真和现实世界试验对系统进行了评估。
URL
https://arxiv.org/abs/2403.15504
