General Place Recognition Survey: Towards Real-World Autonomy

2024-05-08

arXiv.org

AI Summary

Large-scale, long-term autonomous robot navigation in real-world scenarios poses significant challenges to place recognition (PR) regarding robustness, generalization, and scalability. Method: This work pioneers the reformulation of PR systems under the SLAM 2.0 paradigm, establishing a unified evaluation framework to clarify technical gaps and evolutionary trajectories. It integrates deep feature learning, multimodal representation, geometric-semantic joint modeling, and lightweight inference, validated empirically on standard benchmarks and open-source toolchains (e.g., MetaSLAM, GPRS). Contribution/Results: We present the first comprehensive survey spanning algorithms, challenges, applications, datasets, and code; propose the inaugural reproducible and scalable PR roadmap tailored for long-term autonomy; and advance AI-driven PR from lab-scale prototypes toward deployment in real robotic systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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 paper aims to bridge this gap by highlighting the crucial role of PR within the framework of Simultaneous Localization and Mapping (SLAM) 2.0. This new phase in robotic navigation calls for scalable, adaptable, and efficient PR solutions by integrating advanced artificial intelligence (AI) technologies. For this goal, we provide a comprehensive review of the current state-of-the-art (SOTA) advancements in PR, alongside the remaining challenges, and underscore its broad applications in robotics. This paper 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. We conclude with a discussion on PR's future directions and provide a summary of the literature covered at: https://github.com/MetaSLAM/GPRS.

Problem

• Develop scalable place recognition for real-world robotics.
• Integrate AI to enhance SLAM 2.0 navigation systems.
• Address challenges in long-term robotic autonomy.

Innovation

• Integrates AI for scalable place recognition
• Focuses on SLAM 2.0 for robotic navigation
• Reviews SOTA advancements and PR challenges