外观
论文arXiv 2609.273941 个月前
基于空间门控特征相关表示的车载 mmWave 旋转雷达地点识别
原文链接：Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation（arXiv 2609.27394 · 发布日期：2026-09-23）
Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation
针对旋转雷达地点识别中的航向变化与特征关系丢失，SGCA-Net用空间门控相关聚合，在MulRan和HeRCULES上验证。
核心要点
- 航向变化在极坐标雷达表示中表现为环形位移，常规全局聚合易丢失区分相似地点的响应关系。
- SGCA-Net结合旋转鲁棒特征提取与SGCA，学习空间权重并聚合局部响应的成对相关性。
- 在MulRan数据集的城市、校园和开放道路环境中，SGCA-Net持续优于SOTA方法并抗较大航向变化。
- 在HeRCULES数据集上的评估显示，该方法无需微调即可泛化到未见环境和雷达传感器。
验证范围与局限：验证范围为MulRan与HeRCULES数据集上的城市、校园、开放道路及未见环境和雷达传感器。
英文摘要 (Abstract)
Automotive spinning FMCW radar provides dense, $360^\circ$ sensing and remains reliable under poor illumination and adverse weather, making it well-suited to autonomous navigation. Place recognition uses these observations to identify previously visited locations for re-localization and long-term navigation. However, heading changes appear as circular shifts in the polar radar representation, and conventional global aggregation can lose relationships among radar responses that are important for distinguishing similar places. We propose SGCA-Net, a spinning radar place recognition framework that combines rotation-robust feature extraction with Spatially Gated Correlation Aggregation (SGCA). SGCA learns spatial weights to reduce the influence of unstable and ambiguous radar regions, while aggregating pairwise correlations among local responses to preserve informative feature relationships. Experiments on the MulRan dataset show that SGCA-Net consistently outperforms SOTA methods across urban, campus, and open-road environments, while remaining robust to substantial heading variation. Evaluation on the HeRCULES dataset further demonstrates that SGCA-Net generalizes to unseen environments and radar sensors without fine-tuning.
作者：Saimunur Rahman, Sagun Singh Shrestha, Abdelwahed Khamis, Peyman Moghadam
来源
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