# Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation

arXiv: https://arxiv.org/abs/2609.27394 · HTML: https://mlsanity.com/paper/2609.27394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://mlsanity.com/paper/1909.01300/) (2019)
- [Compact Bilinear Pooling](https://mlsanity.com/paper/1511.06062/) (2015)
- [Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization](https://mlsanity.com/paper/1712.01034/) (2017)
- [LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition](https://mlsanity.com/paper/2109.08336/) (2021)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization](https://mlsanity.com/paper/2210.04432/) (2022)
- [AutoPlace: Robust Place Recognition with Single-chip Automotive Radar](https://mlsanity.com/paper/2109.08652/) (2021)
- [Pair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers](https://mlsanity.com/paper/2410.06614/) (2024)
- [RadarSLAM: Radar based Large-Scale SLAM in All Weathers](https://mlsanity.com/paper/2005.02198/) (2020)
- [Are We Ready for Radar to Replace Lidar in All-Weather Mapping and Localization?](https://mlsanity.com/paper/2203.10174/) (2022)
- [RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold](https://mlsanity.com/paper/2307.04321/) (2023)
- [GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors](https://mlsanity.com/paper/2308.04638/) (2023)
- [Open-RadVLAD: Fast and Robust Radar Place Recognition](https://mlsanity.com/paper/2401.15380/) (2024)
- [HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for Multi-session Radar SLAM](https://mlsanity.com/paper/2502.01946/) (2025)
- [SHeRLoc: Synchronized Heterogeneous Radar Place Recognition for Cross-Modal Localization](https://mlsanity.com/paper/2506.15175/) (2025)
- [Large-Scale Topological Radar Localization Using Learned Descriptors](https://mlsanity.com/paper/2110.03081/) (2021)

## Abstract (arXiv)

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.