Going Places: Place Recognition in Artificial and Natural Systems
It addresses the problem of place recognition for autonomous systems and biological navigation, but it is incremental as it reviews existing knowledge without presenting new experimental results.
This review synthesizes findings from robotic systems, animal studies, and human research to explore how different systems encode and recall place, aiming to foster innovations in artificial localization by connecting future developments to insights from biological navigation.
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.