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arXiv · 2511.03165

SENT Map -- Semantically Enhanced Topological Maps with Foundation Models

Raj Surya Rajendran Kathirvel·Zach A Chavis·Stephen J. Guy·Karthik Desingh
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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.

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Raj Surya Rajendran Kathirvel, Zach A Chavis, Stephen J. Guy, Karthik Desingh. 2025-11-05. SENT Map -- Semantically Enhanced Topological Maps with Foundation Models. https://arxiv.org/abs/2511.03165

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