Ground Penetrating Radar Image Processing Towards Underground Utilities Detection for Robotic Applications

During the last decades, the development of powerful novel sensor topologies, namely Ground Penetrating Radar (GPR) antennas, gave a thrust in the modeling of underground space. Subsurface mapping is of particular importance for future robotic applications that aim to operate in underground space. However, the current processing methods of GPR images (B-Scans) towards detecting underground utilities is typically laborious, semi-automatic and prone to errors, especially on cluttered subsurface sceneries that produce unclear signatures, and still require the manual annotation of experts. Due to lack of large scale datasets from such sceneries, the adoption of deep learning and model specific methods with increased repeatability is not feasible yet. In this scope, and working towards subsurface mapping for underground robotic applications, the paper at hand introduces an application specific method tailored to operate with realistic GPR data for the automatic detection of underground utilities, by applying a hyperbola detection framework on the radar images obtained from a surface GPR. To achieve this, a segmentation step on the GPR data is applied to prepare them for hand-designed feature extraction. The extracted representative features are used to train a SVM, while a final fitting step is applied to the detected hyperbola segments. The developed methodology has been evaluated on cluttered radar images that correspond to a subsurface area with dense buried pipes and exhibited remarkable performance.