Underground Mapping and Localization Based on Ground-Penetrating Radar
<|reference_start|>Underground Mapping and Localization Based on Ground-Penetrating Radar: 3D object reconstruction based on deep neural networks has gained increasing attention in recent years. However, 3D reconstruction of underground objects to generate point cloud maps remains a challenge. Ground Penetrating Radar (GPR) is one of the most powerful and extensively used tools for detecting and locating underground objects such as plant root systems and pipelines, with its cost-effectiveness and continuously evolving technology. This paper introduces a parabolic signal detection network based on deep convolutional neural networks, utilizing B-scan images from GPR sensors. The detected keypoints can aid in accurately fitting parabolic curves used to interpret the original GPR B-scan images as cross-sections of the object model. Additionally, a multi-task point cloud network was designed to perform both point cloud segmentation and completion simultaneously, filling in sparse point cloud maps. For unknown locations, GPR A-scan data can be used to match corresponding A-scan data in the constructed map, pinpointing the position to verify the accuracy of the map construction by the model. Experimental results demonstrate the effectiveness of our method.<|reference_end|>
@article{zhang2024underground,
    title={Underground Mapping and Localization Based on Ground-Penetrating Radar},
    author={Jinchang Zhang, Guoyu Lu},
    journal={arXiv preprint arXiv:2409.16446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16446},
    primaryClass={cs.CV}
}