发表机构
CSIRO Robotics, CSIRO; Queensland University of Technology（CSIRO机器人部门，澳大利亚联邦科学与工业研究组织; 昆士兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对旋转雷达位置识别中航向变化导致极坐标循环移位及全局聚合丢失特征关系的问题，提出SGCA-Net，结合旋转鲁棒特征提取与空间门控相关聚合，在MulRan和HeRCULES数据集上优于现有方法。
AI 中文摘要
车载旋转调频连续波雷达提供密集的360°感知，并在光照不良和恶劣天气下保持可靠，使其非常适合自主导航。位置识别利用这些观测来识别先前访问过的位置，以支持重定位和长期导航。然而，航向变化在极坐标雷达表示中表现为循环移位，而传统的全局聚合可能丢失雷达响应之间对于区分相似位置至关重要的关系。我们提出SGCA-Net，一种旋转雷达位置识别框架，该框架将旋转鲁棒特征提取与空间门控相关聚合（SGCA）相结合。SGCA学习空间权重以减少不稳定和模糊雷达区域的影响，同时聚合局部响应之间的成对相关性以保留信息丰富的特征关系。在MulRan数据集上的实验表明，SGCA-Net在城市、校园和开放道路环境中始终优于最先进方法，同时对显著的航向变化保持鲁棒性。在HeRCULES数据集上的评估进一步证明，SGCA-Net无需微调即可泛化到未见环境和雷达传感器。
英文摘要
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.
CommentsAccepted at the 28th International Conference on Digital Image Computing: Techniques and Applications (DICTA 2026). 8 pages, 3 figures
