Semantic Mapping in Indoor Embodied AI -- A Comprehensive Survey and Future Directions

2025-01-10

AI Summary

To address the high memory overhead and computational inefficiency in semantic mapping for embodied agents operating long-term in unknown indoor environments, this paper presents the first comprehensive, multi-dimensional survey tailored to indoor scenarios. We systematically classify existing approaches along two orthogonal dimensions—structural representation (grid-based, topological, point-cloud, or hybrid) and information type (implicit features vs. explicit data)—and critically analyze state-of-the-art methods, including deep semantic segmentation, SLAM-semantic integration, cross-modal alignment, graph neural network (GNN)-based modeling, and 3D reconstruction, identifying their technical limits and bottlenecks. We propose an evolutionary paradigm for semantic maps characterized by open-vocabulary support, queryability, and task-agnosticism. Finally, we distill four key future research directions. This work establishes a theoretical framework and technical roadmap for advancing semantic understanding, long-horizon navigation, and task planning in embodied intelligence.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Semantic Mapping
• Memory Efficiency
• Computational Efficiency

Innovation

• Semantic Mapping
• Robotics Navigation
• Memory Efficiency