用雷达数据实现地下物体3D建图与定位，精度高且可实时验证。
Underground Mapping and Localization Based on Ground-Penetrating Radar
- 基于深度网络检测雷达图像中的抛物线特征点
- 同步完成点云分割与补全，解决地下数据稀疏问题
- 支持未知位置匹配，可验证建图准确性，适合工程探测
近年来，基于深度神经网络的3D物体重建受到广泛关注。然而，利用地下物体生成点云地图仍具挑战性。地面穿透雷达（GPR）是检测和定位地下物体（如植物根系、管道）的重要工具，具有成本低、技术持续进步的优势。本文提出一种基于深度卷积神经网络的抛物线信号检测网络，利用GPR传感器获取的B-scan图像。检测到的关键点可用于精确拟合抛物线曲线，将原始的GPR B-scan图像解释为物体模型的横截面。此外，设计了一种多任务点云网络，可同时进行点云分割与补全，以填充稀疏的点云地图。对于未知位置，可通过GPR A-scan数据与构建地图中的对应数据匹配，精确定位并验证建图准确性。实验结果表明该方法有效。
原文摘要 · Abstract (English)
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.
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