Going Places: Place Recognition in Artificial and Natural Systems

TL;DR

This review compares biological and artificial place recognition systems, highlighting shared strategies and proposing a unified framework to advance autonomous localization by integrating insights from animals, humans, and machines.

Contribution

It synthesizes cross-disciplinary findings to develop a unified conceptual framework for place recognition, bridging biological and artificial systems.

Findings

Animals use multimodal cues for navigation

Humans incorporate semantic and cultural factors

Artificial systems employ scalable, data-driven models

Abstract

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.