Radar is highlighted for robust sensing capabilities in adverse weather conditions (e.g. dense fog, heavy rain, or snowfall). In addition, Radar can cover wide areas and penetrate small particles. Despite these advantages, Radar-based place recognition remains in the early stages compared to other sensors due to its unique characteristics such as low resolution, and significant noise. In this paper, we propose a Radarbased place recognition utilizing a descriptor called ReFeree using a feature and free space. Unlike traditional methods, we overwhelmingly summarize the Radar image. Despite being lightweight, it contains semi-metric information and is also outstanding from the perspective of place recognition performance. For concrete validation, we test a single session from the MulRan dataset and a multi-session from the Oxford Offroad Radar, Oxford Radar RobotCar, and the Boreas dataset.

翻译：摘要：雷达因其在恶劣天气条件（如浓雾、暴雨或降雪）下的鲁棒感知能力而备受关注。此外，雷达可以覆盖广阔区域并穿透微小颗粒。尽管具有这些优势，但由于雷达的独特特性（如低分辨率和显著噪声），基于雷达的地点识别相较于其他传感器仍处于早期阶段。本文提出了一种基于雷达的地点识别方法，利用名为ReFeree的、包含特征与自由空间的描述子。与传统方法不同，我们对雷达图像进行了高度概括。尽管该描述子轻量化，但其包含了半度量信息，并且在地点识别性能方面表现出色。为进行具体验证，我们在MulRan数据集的单会话、牛津越野雷达、牛津雷达机器人车及博雷亚斯数据集的多会话上进行了测试。