Loop Closure Detection (LCD) is an essential task in robotics and computer vision, serving as a fundamental component for various applications across diverse domains. These applications encompass object recognition, image retrieval, and video analysis. LCD consists in identifying whether a robot has returned to a previously visited location, referred to as a loop, and then estimating the related roto-translation with respect to the analyzed location. Despite the numerous advantages of radar sensors, such as their ability to operate under diverse weather conditions and provide a wider range of view compared to other commonly used sensors (e.g., cameras or LiDARs), integrating radar data remains an arduous task due to intrinsic noise and distortion. To address this challenge, this research introduces RadarLCD, a novel supervised deep learning pipeline specifically designed for Loop Closure Detection using the FMCW Radar (Frequency Modulated Continuous Wave) sensor. RadarLCD, a learning-based LCD methodology explicitly designed for radar systems, makes a significant contribution by leveraging the pre-trained HERO (Hybrid Estimation Radar Odometry) model. Being originally developed for radar odometry, HERO's features are used to select key points crucial for LCD tasks. The methodology undergoes evaluation across a variety of FMCW Radar dataset scenes, and it is compared to state-of-the-art systems such as Scan Context for Place Recognition and ICP for Loop Closure. The results demonstrate that RadarLCD surpasses the alternatives in multiple aspects of Loop Closure Detection.

翻译：摘要：闭环检测是机器人与计算机视觉领域的一项关键任务，作为跨领域多种应用的基础组件，涵盖目标识别、图像检索及视频分析等场景。该技术旨在判断机器人是否已返回先前访问过的位置（即闭环），并估计相对于该位置的旋转平移关系。尽管雷达传感器具有在多样化天气条件下工作、视场范围优于常用传感器（如摄像头或激光雷达）等显著优势，但其固有噪声与畸变特性使得雷达数据集成仍面临严峻挑战。为应对这一难题，本研究提出RadarLCD——一种专为调频连续波雷达传感器设计的新型监督深度学习闭环检测流水线。作为专用于雷达系统的基于学习的闭环检测方法，RadarLCD通过利用预训练的HERO（混合估计雷达里程计）模型作出重要贡献。该模型最初为雷达里程计而开发，其提取的特征被用于选择闭环检测任务的关键点。本方法在多种调频连续波雷达数据集场景下进行评估，并与场景识别领域的Scan Context及闭环检测领域的ICP等先进系统进行对比。实验结果表明，RadarLCD在闭环检测的多项指标上均优于现有方案。