Despite the growing adoption of radar in robotics, the majority of research has been confined to homogeneous sensor types, overlooking the integration and cross-modality challenges inherent in heterogeneous radar technologies. This leads to significant difficulties in generalizing across diverse radar data types, with modality-aware approaches that could leverage the complementary strengths of heterogeneous radar remaining unexplored. To bridge these gaps, we propose SHeRLoc, the first deep network tailored for heterogeneous radar, which utilizes RCS polar matching to align multimodal radar data. Our hierarchical optimal transport-based feature aggregation method generates rotationally robust multi-scale descriptors. By employing FFT-similarity-based data mining and adaptive margin-based triplet loss, SHeRLoc enables FOV-aware metric learning. SHeRLoc achieves an order of magnitude improvement in heterogeneous radar place recognition, increasing recall@1 from below 0.1 to 0.9 on a public dataset and outperforming state of-the-art methods. Also applicable to LiDAR, SHeRLoc paves the way for cross-modal place recognition and heterogeneous sensor SLAM. The supplementary materials and source code are available at https://sites.google.com/view/radar-sherloc.

翻译：尽管雷达在机器人领域的应用日益广泛，但大多数研究仍局限于同构传感器类型，忽视了异构雷达技术固有的集成与跨模态挑战。这导致在不同雷达数据类型间泛化存在显著困难，而能够利用异构雷达互补优势的模态感知方法尚未得到探索。为弥合这些差距，我们提出了SHeRLoc——首个专为异构雷达设计的深度网络，其利用RCS极坐标匹配实现多模态雷达数据对齐。我们基于分层最优传输的特征聚合方法可生成旋转鲁棒的多尺度描述符。通过采用基于FFT相似度的数据挖掘和自适应边界三元组损失，SHeRLoc实现了视场感知的度量学习。在异构雷达地点识别任务中，SHeRLoc实现了数量级的性能提升，在公开数据集上将召回率@1从不足0.1提升至0.9，并超越现有最优方法。该方法同样适用于LiDAR，为跨模态地点识别和异构传感器SLAM开辟了新路径。补充材料与源代码详见https://sites.google.com/view/radar-sherloc。