Novel Positioning via Ground Penetrating Radar Image Matching

Positioning technology is crucial for numerous real-world applications, such as navigation, robotics and autonomous driving. However, existing methods like GPS, Wi-Fi and Bluetooth positioning technologies have limitations. such as weak signals, significant interference, limited effective ranges and adverse weather conditions. Visual positioning, on the other hand, is sensitive to lighting conditions and performs poorly when optical features become obscured. Therefore, there is an urgent need for novel solutions to address positioning challenges in complex environments. In this paper, we propose a new positioning system based on enhanced Siamese deep network using ground penetrating radar (GPR) data, aiming to achieve high-precision positioning. The proposed method employs a dual-channel input mechanism to process query images and reference images respectively, extract key features and calculate the similarities, thereby achieving precise positioning. The model incorporates an attention mechanism and enhances feature extraction capabilities by introducing the SE module. The training and verification results on the surface fingerprint data set show that the image matching method based on the Siamese network significantly improves the matching accuracy and positioning ability of GPR data. Experimental results indicate that the proposed system achieved relatively high-precision positioning using single ground penetrating radar modality alone, successfully overcome many limitations faced by existing positioning technologies, thus, it provides a new direction for the development of intelligent positioning and navigation systems. Ultimately, the findings in this work offers a reliable positioning solution for applications such as autonomous driving, intelligent robots, and drone navigation.