Autonomous robotic ground penetrating radar surveys of ice sheets; Using machine learning to identify hidden crevasses
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Williams, Rebecca M., Laura E. Ray, and James H. Lever. “Autonomous Robotic Ground Penetrating Radar Surveys of Ice Sheets; Using Machine Learning to Identify Hidden Crevasses.” 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings, (Manchester, UK),July 16-17, 2012, 7–12. https://doi.org/10.1109/IST.2012.6295593.
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
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.