用雷达实现快速精准的3维定位，适配各种复杂环境。
RadLoc: Radar-based 3-DoF Global Localization via Fast, Robust, and Lightweight Spatial Descriptor Across Diverse Environmental Scenarios
- 设计轻量级空间描述符与分层检索策略，加速定位过程。
- 在5个数据集15组序列上实现最快检索速度和最小描述符尺寸。
- 适合用于SLAM及多时段定位系统，抗干扰能力强。
基于旋转雷达的全局定位因其对恶劣天气和复杂环境的鲁棒性而受到关注，但多数研究仅聚焦于场景识别或位姿估计等单一环节。本文提出一种端到端的雷达全局定位框架RadLoc，涵盖从场景识别到3-DoF位姿估计的全过程。通过1D CA-CFAR滤波加速预处理，并利用雷达图像近距主导特性设计紧凑描述符与高效的粗粒度到细粒度检索策略。结合相位相关法实现3-DoF位姿估计，形成适用于SLAM及多时段SLAM系统的通用模块。在5个数据集共15个序列上的大量实验表明，RadLoc在保持最小微描述符尺寸的同时，拥有最快检索速度，且性能稳定可靠。补充材料详见 https://sparolab.github.io/research/radloc/。
原文摘要 · Abstract (English)
While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies have focused on individual components such as place recognition or pose estimation. In this paper, we take a holistic view of radar sensor-based global localization and present RadLoc, a fast, robust, and lightweight end-to-end pipeline from place recognition to 3-DoF pose estimation. RadLoc accelerates pre-processing using 1D CA-CFAR filtering and leverages the near-range dominance in spinning radar images to design a compact descriptor and an efficient hierarchical coarse-to-fine retrieval strategy. Moreover, coupled with phase correlation-based 3-DoF pose estimation, it forms a versatile global localization module applicable to SLAM and multi-session SLAM systems. Extensive experiments on 15 sequences across 5 datasets demonstrate that RadLoc achieves robust performance while maintaining the smallest descriptor size and fastest retrieval time among state-of-the-art approaches. The supplementary materials are available at https://sparolab.github.io/research/radloc/.
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