首个面向异构雷达的跨模态定位模型，显著提升不同雷达数据间的识别准确率。
SHeRLoc: Synchronized Heterogeneous Radar Place Recognition for Cross-Modal Localization
- 通过雷达散射特性极化匹配对齐多模态雷达数据
- 在公开数据集上召回率@1从不足0.1提升至0.9
- 适用于雷达与激光雷达融合，适合异构传感器定位场景
尽管雷达在机器人领域应用日益广泛，但多数研究仍局限于同质传感器类型，忽视了异构雷达技术带来的集成与跨模态挑战。这导致在不同雷达数据类型间泛化困难，而能利用异构雷达互补优势的模态感知方法尚未被探索。为此，我们提出SHeRLoc，首个专为异构雷达设计的深度网络，采用雷达散射截面（RCS）极化匹配对齐多模态雷达数据。其基于分层最优传输的特征聚合方法生成旋转鲁棒的多尺度描述符。结合基于FFT相似性的数据挖掘与自适应边界三元组损失，实现视场（FOV）感知的度量学习。SHeRLoc在公开数据集上将召回率@1从低于0.1提升至0.9，性能超越现有最优方法。该方法亦适用于激光雷达，为跨模态定位与异构传感器SLAM开辟新路径。补充材料与源代码见 https://sites.google.com/view/radar-sherloc。
原文摘要 · Abstract (English)
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.
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