[Paper Review] General Place Recognition Survey: Towards the Real-world Autonomy Age

Peng Yin, Shiqi Zhao+7 more|arXiv (Cornell University)|Sep 9, 2022

Indoor and Outdoor Localization Technologies17 citations

TL;DR

This survey presents a comprehensive review of long-term place recognition (GPR) for real-world robotics autonomy, covering multi-modal sensor approaches, deep learning-based feature extraction, and robustness to appearance and viewpoint changes. It introduces a unified framework, new datasets, and an evaluation API to bridge the gap between theory and real-world deployment in large-scale, long-term navigation systems.

ABSTRACT

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and application in multiple fields such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efficiency in real-world applications. In this work, we survey the state-of-the-art methods that target long-term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.

Motivation & Objective

• Address the lack of a unified framework for long-term place recognition (GPR) that handles appearance changes, viewpoint differences, unknown areas, and real-time efficiency.
• Review state-of-the-art methods across visual, LiDAR, radar, and multi-modal sensor inputs for robust place recognition.
• Identify key challenges in real-world deployment, including temporal appearance variations, viewpoint shifts, and unknown environment robustness.
• Provide a standardized benchmark with new datasets and an evaluation API to enable fair comparison and advancement of GPR methods.
• Guide researchers toward generalizable, real-world applicable place recognition systems for long-term autonomy in robotics.

Proposed method

• Formulate place recognition as a general, appearance- and viewpoint-invariant retrieval problem across diverse sensor modalities.
• Survey deep learning-based feature extraction techniques, including 360°-aware convolutional layers and transformer-based feature aggregation for multi-modal inputs.
• Introduce domain adaptation and sim-to-real transfer methods to enable few-shot training with high re-localization accuracy over hundreds of kilometers.
• Develop a multi-sensor fusion strategy combining visual, LiDEDAR, and radar data to compensate for modality-specific limitations (e.g., texture loss in LiDAR, noise in radar).
• Design a standardized evaluation API and release new large-scale, long-term datasets for city-scale and indoor environments with temporal and viewpoint variations.
• Integrate metrics for robustness, efficiency, and generalization across unknown and challenging environments.

Experimental results

Research questions

• RQ1 How can place recognition systems achieve robust performance across large-scale, long-term environments with significant appearance and viewpoint variations?
• RQ2 What are the most effective multi-modal sensor fusion strategies to improve generalization and robustness in real-world place recognition?
• RQ3 To what extent can sim-to-real transfer and few-shot learning enable accurate re-localization with minimal real-world training data?
• RQ4 How can a standardized benchmark and evaluation framework improve comparability and progress in long-term place recognition research?
• RQ5 What role does place recognition play in enabling lifelong, continual perception and navigation for autonomous robots in unstructured environments?

Key findings

• Multi-modal sensor fusion—especially combining visual, LiDAR, and radar—significantly improves robustness to environmental changes and sensor-specific limitations.
• Deep learning-based feature extractors with 360°-aware convolutions and transformer-based attention mechanisms achieve high accuracy in viewpoint-invariant place recognition.
• Sim-to-real domain adaptation enables accurate re-localization over hundreds of kilometers using only a few kilometers of real-world training data.
• The proposed evaluation API and new datasets (e.g., for city-scale and indoor long-term localization) provide a standardized benchmark for fair comparison across methods.
• Reliable place recognition enables long-term navigation in complex environments, including GPS-denied settings like underground or extraterrestrial surfaces.
• The integration of place recognition into SLAM and multi-agent systems enhances data association, localization, and continual perception capabilities.