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Method for Visual Localization of Oil and Gas Wellhead Based on Distance Function of Projected Features
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作者 Ying Xie Xiang-Dong Yang +2 位作者 Zhi Liu Shu-Nan Ren Ken Chen 《International Journal of Automation and computing》 EI CSCD 2017年第2期147-158,共12页
A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based local... A localization method based on distance function of projected features is presented to solve the accuracy reduction or failure problem due to occlusion and blurring caused by smog, when dealing with vision based localization for target oil and gas wellhead (OGWH). Firstly, the target OGWH is modeled as a cylinder with marker, and a vector with redundant parameter is used to describe its pose. Secondly, the explicit mapping relationship between the pose vector with redundant parameter and projected features is derived. Then, a 2D-point-to-feature distance function is proposed, as well as its derivative. Finally, based on this distance function and its derivative, an algorithm is proposed to estimate the pose of target OGWH directly according to the 2D image information, and the validity of the method is verified by both synthetic data and real image experiments. The results show that this method is able to accomplish the localization in the case of occlusion and blurring, and its anti-noise ability is good especially with noise ratio of less than 70%. 展开更多
关键词 Robot vision visual localization 3D object localization model based pose estimation distance function of projectedfeatures nonlinear least squares random sample consensus (RANSAC).
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运用组合加权距离函数的多指标面板数据聚类方法及应用
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作者 王少波 贾来喜 《武警工程大学学报》 2015年第6期1-4,共4页
针对多指标面板数据聚类分析的样品分类和空间地域划分,提出了一个多指标面板数据的综合聚类分析方法。从多元统计和层次分析角度,构造了多指标面板数据的样本距离函数。通过实证分析证明,该方法在能够满足系统分析的统一性要求的同... 针对多指标面板数据聚类分析的样品分类和空间地域划分,提出了一个多指标面板数据的综合聚类分析方法。从多元统计和层次分析角度,构造了多指标面板数据的样本距离函数。通过实证分析证明,该方法在能够满足系统分析的统一性要求的同时,保证指标之间的不相关;能够克服时间维度上均值处理的缺陷,信息损失较少;体现了指标间的主次差别。 展开更多
关键词 多指标面板数据 聚类分析 样本距离函数
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