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This paper presents SGCA-Net, a novel framework for automotive spinning radar place recognition that addresses the challenge of heading-induced circular shifts in polar representations. By integrating rotation-robust feature extraction with spatially gated correlation aggregation, the method effectively preserves critical feature relationships while filtering unstable regions to improve localization accuracy across diverse environments. The approach demonstrates strong generalization capabilities on unseen datasets without requiring fine-tuning, offering a robust solution for autonomous navigation under adverse conditions.
Automotive mmWave Spinning Radar Place Recognition with Spatially Gated Feature-Correlation Representation
arXiv:2609.27394v1 Announce Type: cross Abstract: 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.
Abstract: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.
Accepted at the 28th International Conference on Digital Image Computing: Techniques and Applications (DICTA 2026). 8 pages, 3 figures
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.27394 [cs.RO]
(or arXiv:2609.27394v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.27394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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