General Place Recognition Survey: Toward Real-World Autonomy

Abstract

In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology. Despite the PR community's remarkable strides over the past two decades, garnering attention from fields like computer vision and robotics, the development of PR methods that sufficiently support real-world robotic systems remains a challenge. This article aims to bridge this gap by highlighting the crucial role of PR within the framework of simultaneous localization and mapping 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence technologies. For this goal, we provide a comprehensive review of the current state-of-the-art advancements in PR, alongside the remaining challenges, and underscore its broad applications in robotics. This article begins with an exploration of PR's formulation and key research challenges. We extensively review literature, focusing on related methods on place representation and solutions to various PR challenges. Applications showcasing PR's potential in robotics, key PR datasets, and open-source libraries are discussed.