SymboSLAM: Semantic Map Generation in a Multi-Agent System
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916174549221376 |
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| author | Colelough, Brandon Curtis |
| author_facet | Colelough, Brandon Curtis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_15504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SymboSLAM: Semantic Map Generation in a Multi-Agent System Colelough, Brandon Curtis Artificial Intelligence Multiagent Systems 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. |
| title | SymboSLAM: Semantic Map Generation in a Multi-Agent System |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2403.15504 |