Spatial–Temporal U-Net for Localizing Ground-Penetrating Radar

As a promising technology for autonomous driving, localizing ground-penetrating radar (LGPR) is a vehicle localization method that relies on prior maps and couples deeply with subsurface features. However, the unique characteristics of GPR data often lead to a significant number of mismatched candidates during localization. Previous learning-based GPR place recognition methods have primarily relied on 2-D convolutional neural networks (CNNs), which struggle to effectively capture critical temporal information, limiting further performance improvements. To address this limitation, we propose a spatial-temporal U-shaped network (STU-Net) that leverages 3-D CNNs to simultaneously extract spatial and temporal features from GPR image sequences. In addition, residual dense blocks (RDBs) are integrated into the network to enable multiscale feature extraction. Extensive experiments conducted on publicly available datasets demonstrate that our STU-Net achieves state-of-the-art performance, outperforming existing methods with significant improvements.