对比生物与人工系统，揭示地点识别的共性机制与挑战
Going Places: Place Recognition in Artificial and Natural Systems
- 跨系统比较机器人、动物和人类的地点识别策略
- 发现拓扑地图、多模态融合等共性方法
- 适合智能导航与认知计算研究者参考
地点识别——识别曾访问过的位置的能力——对生物导航和自主系统均至关重要。本文综述了机器人系统、动物研究和人类认知研究中的成果，探讨不同系统如何编码与回忆地点。我们分析了人工系统、动物与人类在计算与表征策略上的异同，凸显拓扑地图构建、线索整合与记忆管理等趋同解决方案。动物系统展现出适应环境的多模态导航演化机制，而人类研究则揭示语义化地点概念、文化影响及内省能力的独特性。人工系统则体现出可扩展架构与数据驱动模型的优势。本文提出一套统一的概念框架，用于理解与发展地点识别机制，并指出泛化能力、鲁棒性与环境变化适应等关键挑战。本综述旨在推动人工定位技术的创新，通过连接动物导航与人类空间认知研究，为未来人工地点识别系统的发展提供启示。
原文摘要 · Abstract (English)
Place recognition, the ability to identify previously visited locations, is critical for both biological navigation and autonomous systems. This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place. We examine the computational and representational strategies employed across artificial systems, animals, and humans, highlighting convergent solutions such as topological mapping, cue integration, and memory management. Animal systems reveal evolved mechanisms for multimodal navigation and environmental adaptation, while human studies provide unique insights into semantic place concepts, cultural influences, and introspective capabilities. Artificial systems showcase scalable architectures and data-driven models. We propose a unifying set of concepts by which to consider and develop place recognition mechanisms and identify key challenges such as generalization, robustness, and environmental variability. This review aims to foster innovations in artificial localization by connecting future developments in artificial place recognition systems to insights from both animal navigation research and human spatial cognition studies.
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