{"title":"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","authors":"R. M. Williams, L. E. Ray, J. Lever","doi":"10.1109/IST.2012.6295593","DOIUrl":null,"url":null,"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.","PeriodicalId":213330,"journal":{"name":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Autonomous robotic ground penetrating radar surveys of ice sheets; Using machine learning to identify hidden crevasses\",\"authors\":\"R. M. Williams, L. E. Ray, J. Lever\",\"doi\":\"10.1109/IST.2012.6295593\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":213330,\"journal\":{\"name\":\"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IST.2012.6295593\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2012.6295593","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.