# Semantic Mapping in Indoor Embodied AI -- A Survey on Advances, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2501.05750 · HTML: https://mlsanity.com/paper/2501.05750/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 12 of 12)

- [TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2602.02459/) (2026)
- [FOM-Nav: Frontier-Object Maps for Object Goal Navigation](https://mlsanity.com/paper/2512.01009/) (2025)
- [GeomPrompt: Geometric Prompt Learning for RGB-D Semantic Segmentation Under Missing and Degraded Depth](https://mlsanity.com/paper/2604.11585/) (2026)
- [eMEM: A Hybrid Spatio-Temporal Memory System For Embodied Agents](https://mlsanity.com/paper/2606.03374/) (2026)
- [EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2606.03509/) (2026)
- [The Embodiment Gap in Robot Foundation Models](https://mlsanity.com/paper/2608.18433/) (2026)
- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)
- [Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping](https://mlsanity.com/paper/2605.23160/) (2026)
- [Vision-Language Model Reasoning for Contextual Semantic Mapping in Intralogistics](https://mlsanity.com/paper/2606.24814/) (2026)
- [When Multi-Robot Systems Meet Agentic AI:Towards Embodied Collective Intelligence](https://mlsanity.com/paper/2606.27929/) (2026)
- [Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding](https://mlsanity.com/paper/2607.10372/) (2026)
- [ActiveLang: Active Open-Vocabulary 3D Mapping with Semantic-Uncertainty-Guided Exploration](https://mlsanity.com/paper/2610.09518/) (2026)

## Abstract (arXiv)

Intelligent embodied agents (e.g. robots) need to perform complex semantic tasks in unfamiliar environments. Among many skills that the agents need to possess, building and maintaining a semantic map of the environment is most crucial in long-horizon tasks. A semantic map captures information about the environment in a structured way, allowing the agent to reference it for advanced reasoning throughout the task. While existing surveys in embodied AI focus on general advancements or specific tasks like navigation and manipulation, this paper provides a comprehensive review of semantic map-building approaches in embodied AI, specifically for indoor navigation. We categorize these approaches based on their structural representation (spatial grids, topological graphs, dense point-clouds or hybrid maps) and the type of information they encode (implicit features or explicit environmental data). We also explore the strengths and limitations of the map building techniques, highlight current challenges, and propose future research directions. We identify that the field is moving towards developing open-vocabulary, queryable, task-agnostic map representations, while high memory demands and computational inefficiency still remaining to be open challenges. This survey aims to guide current and future researchers in advancing semantic mapping techniques for embodied AI systems.