Topo-Field：基于大脑启发的层次化布局-物体-位置场的拓扑度量地图构建
Topo-Field: Topometric mapping with Brain-inspired Hierarchical Layout-Object-Position Fields
AI总结：
受大脑POR区群体编码启发，提出Topo-Field框架，通过将布局-物体-位置关联集成到神经场并利用大型基础模型免标注训练，构建兼具语义丰富性与计算效率的拓扑度量地图，有效实现高保真场景理解与高效机器人导航的桥接。
摘要：
移动机器人需要全面的场景理解，以便在多样化的环境中有效运行，并辅以布局、物体及其关系等上下文信息。尽管神经辐射场（NeRFs）等进展提供了高保真度的3D重建，但它们计算量大，且通常缺乏规划和导航所必需的可通行空间的高效表示。相比之下，拓扑地图计算效率高，但缺乏更完整理解环境所需的语义丰富性。受后鼻皮质（POR）中一种群体编码的启发，该编码对场景内容上的空间布局具有强适应性，能快速形成高级认知地图。本研究引入了Topo-Field，这是一个将布局-物体-位置（LOP）关联集成到神经场中，并从该学习到的表示中构建拓扑度量地图的框架。LOP关联通过显式编码物体和布局信息进行建模，而大型基础模型（LFM）技术则允许在无需大量标注的情况下进行高效训练。随后，通过查询学习到的神经表示来构建拓扑度量地图，同时提供了语义丰富性和计算效率。在多房间环境中的实证评估证明了Topo-Field在位置属性推断、查询定位和拓扑度量规划等任务中的有效性，成功弥合了高保真场景理解与高效机器人导航之间的差距。
英文摘要：
Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. Although advances like neural radiation fields (NeRFs) offer high-fidelity 3D reconstructions, they are computationally intensive and often lack efficient representations of traversable spaces essential for planning and navigation. In contrast, topological maps are computationally efficient but lack the semantic richness necessary for a more complete understanding of the environment. Inspired by a population code in the postrhinal cortex (POR) strongly tuned to spatial layouts over scene content rapidly forming a high-level cognitive map, this work introduces Topo-Field, a framework that integrates Layout-Object-Position (LOP) associations into a neural field and constructs a topometric map from this learned representation. LOP associations are modeled by explicitly encoding object and layout information, while a Large Foundation Model (LFM) technique allows for efficient training without extensive annotations. The topometric map is then constructed by querying the learned neural representation, offering both semantic richness and computational efficiency. Empirical evaluations in multi-room environments demonstrate the effectiveness of Topo-Field in tasks such as position attribute inference, query localization, and topometric planning, successfully bridging the gap between high-fidelity scene understanding and efficient robotic navigation.
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。
