# SHeRLoc: Synchronized Heterogeneous Radar Place Recognition for Cross-Modal Localization

arXiv: https://arxiv.org/abs/2506.15175 · HTML: https://mlsanity.com/paper/2506.15175/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2025.

## Built on by (most cited first, 4 of 4)

- [RLPR: Radar-to-LiDAR Place Recognition via Two-Stage Asymmetric Cross-Modal Alignment for Autonomous Driving](https://mlsanity.com/paper/2603.07920/) (2026)
- [Spatially Stratified Distillation for Heterogeneous Radar Place Recognition](https://mlsanity.com/paper/2606.18687/) (2026)
- [Geometry-Conditioned Visual Place Recognition in Natural Environments](https://mlsanity.com/paper/2609.27370/) (2026)
- [Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation](https://mlsanity.com/paper/2609.27394/) (2026)

## Abstract (arXiv)

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.