4D RadarPR: Context-Aware 4D Radar Place Recognition in harsh scenarios

Abstract

Place recognition is a fundamental technology for uncrewed systems such as robots and autonomous vehicles, enabling tasks like global localization and simultaneous localization and mapping (SLAM). Existing Place recognition technologies based on vision or LiDAR have made significant progress, but these sensors may degrade or fail in adverse conditions. 4D millimeter-wave radar offers strong resistance to particles like smoke, fog, rain, and snow, making it a promising option for robust scene perception and localization. Therefore, we explore the characteristics of 4D radar point clouds and propose a novel Context-Aware 4D Radar Place Recognition (4D RadarPR) method for adverse scenarios. Specifically, we first adopt a point-based feature extraction (PFE) module to capture raw point cloud information. On top of PFE, we propose a multi-scale context information fusion (MCIF) module to achieve local feature extraction at different scales and adaptive fusion. To capture global spatial relationships and integrate contextual information, the MCIF module introduces a fusion block based on multi-head cross-attention to combine point-wise features with local spatial features. Additionally, we explore the role of Radar Cross Section (RCS) information in enhancing the discriminability of descriptors and propose a local RCS relation-guided attention network to enhance local features before generating the global descriptor. Extensive experiments are conducted on in-house datasets and public datasets, covering various scenarios and including both long-range and short-range radar data. We compared the proposed method with several state-of-the-art approaches, including BevPlace++, LSP-Net, and Transloc4D, and achieved the best overall performance. Notably, on long-range radar data, our method achieved an average Recall@1 of 89.9%, outperforming the second-best method by 1.9%. Furthermore, our method demonstrated acceptable generalization ability across diverse scenarios, showcasing its robustness.