{"ID":2845632,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03165","arxiv_id":"2511.03165","title":"SENT Map -- Semantically Enhanced Topological Maps with Foundation Models","abstract":"We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environment in a JSON text format, we enable semantic information to be added and edited in a format that both humans and FMs understand, while grounding the robot to existing nodes during planning to avoid infeasible states during deployment. Our proposed framework employs a two stage approach, first mapping the environment alongside an operator with a Vision-FM, then using the SENT-Map representation alongside a natural-language query within an FM for planning. Our experimental results show that semantic-enhancement enables even small locally-deployable FMs to successfully plan over indoor environments.","short_abstract":"We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environment in a JSON text format, we enable semantic information to be added...","url_abs":"https://arxiv.org/abs/2511.03165","url_pdf":"https://arxiv.org/pdf/2511.03165v1","authors":"[\"Raj Surya Rajendran Kathirvel\",\"Zach A Chavis\",\"Stephen J. Guy\",\"Karthik Desingh\"]","published":"2025-11-05T04:22:04Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}