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Robotic Subsurface Mapping Using Ground Penetrating Radar by Herman Herman (1997).pdf

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Abstract

The goal of our research is to develop an autonomous robot for subsurface mapping. We are motivated by the growing need for mapping buried pipes, hazardous waste, landmines and other buried objects. Most of these are large scale mapping problems, and to manually construct subsurface maps in these cases would require a significant amount of resources. Therefore, automating the subsurface mapping process is an important factor in alleviating these problems. To achieve our goal, we have developed a robotic system that can autonomously gather and process Ground Penetrating Radar (GPR) data. The system uses a canning laser rangefinder to construct an elevation map of an area. By using the elevation map, a robotic manipulator can follow the contour of the terrain when it moves the GPR antenna during the scanning process. The collected data are then processed to detect and locate buried objects. We have developed three new processing methods, two are volume ased processing methods and one is a surface based processing method. In volume based processing, the 3-D data are directly processed to find the buried objects, while in surface based processing, the 3-D data are first reduced to a series of 2.5-D surfaces before further processing. Each of these methods has its own strengths and weaknesses. The volume based processing methods can be made very fast using parallel processing techniques, but they require an accurate propagation velocity of the GPR signal in the soil. On the other hand, the surface based processing method uses 3-D segmentation to recognize the shape of the buried objects, which does not require an accurate propagation velocity

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