自主机器人探地雷达冰盖调查;使用机器学习来识别隐藏的裂缝

R. M. Williams, L. E. Ray, J. Lever
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引用次数: 12

摘要

本文提出了继续开发完全自主的机器人系统的方法,该系统利用探地雷达对冰川地下进行成像。我们使用完善的机器学习算法和适当的无偏处理,特别是那些也适用于实时图像分析和检测的算法。2010年,我们的机器人和一台Pisten Bully拖拉机在麦克默多站附近的剪切区收集了15幅南极GPR图像,并结合支持向量机(SVM)对三种处理方案进行了测试和评估。使用改进的交叉验证技术,我们使用径向基核支持向量机训练并评估下采样和纹理映射的裂缝GPR图像,与使用原始数据的60%分类率相比,正确地分类了所有示例。我们还在一个更大的数据集上测试了最成功的处理方案,该数据集由相同部署中记录的94张裂缝过境的GPR图像组成。我们的实验证明了机器人GPR成像调查实时目标检测和分类的前景和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Autonomous robotic ground penetrating radar surveys of ice sheets; Using machine learning to identify hidden crevasses
This paper presents methods to continue development of a completely autonomous robotic system employing ground penetrating radar imaging of the glacier sub-surface. We use well established machine learning algorithms and appropriate un-biased processing, particularly those which are also suitable for real-time image analysis and detection. We tested and evaluated three processing schemes in conjunction with a Support Vector Machine (SVM) trained on 15 examples of Antarctic GPR imagery, collected by our robot and a Pisten Bully tractor in 2010 in the shear zone near McMurdo Station. Using a modified cross validation technique, we correctly classified all examples with a radial basis kernel SVM trained and evaluated on down-sampled and texture-mapped GPR images of crevasses, compared to 60% classification rate using raw data. We also test the most successful processing scheme on a larger dataset, comprised of 94 GPR images of crevasse crossings recorded in the same deployment. Our experiments demonstrate the promise and reliability of real-time object detection and classification with robotic GPR imaging surveys.
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