SymboSLAM: Semantic Map Generation in a Multi-Agent System
<|reference_start|>SymboSLAM: Semantic Map Generation in a Multi-Agent System: 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.<|reference_end|>
@article{colelough2024symboslam:,
    title={SymboSLAM: Semantic Map Generation in a Multi-Agent System},
    author={Brandon Curtis Colelough},
    journal={arXiv preprint arXiv:2403.15504},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15504},
    primaryClass={cs.AI cs.MA}
}