室内具身智能中的语义建图——关于进展、挑战与未来方向的综述
Semantic Mapping in Indoor Embodied AI -- A Survey on Advances, Challenges, and Future Directions
AI总结：
本文综述了具身智能中专门用于室内导航的语义地图构建方法，按结构表示和编码信息类型进行分类，指出开放词汇、可查询的地图表示是未来方向，而高内存与低计算效率仍是主要挑战。
摘要：
智能具身智能体（例如机器人）需要在陌生环境中执行复杂的语义任务。在智能体需要具备的众多技能中，构建和维护环境的语义地图在长时程任务中最为关键。语义地图以结构化的方式捕获环境信息，使智能体能够在整个任务过程中参考它进行高级推理。虽然现有关于具身智能的综述侧重于总体进展或导航和操作等特定任务，但本文对具身智能中专门用于室内导航的语义地图构建方法进行了全面综述。我们根据这些方法的结构表示（空间网格、拓扑图、密集点云或混合地图）及其编码的信息类型（隐式特征或显式环境数据）对其进行分类。我们还探讨了地图构建技术的优势与局限性，强调了当前的挑战，并提出了未来的研究方向。我们发现该领域正朝着开发开放词汇、可查询、任务无关的地图表示方向发展，而高内存需求和计算效率低下仍然是亟待解决的开放挑战。本综述旨在指导当前和未来的研究人员推进具身智能系统的语义建图技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
