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Fitting a Parametric Model to a Cloud of Points via Optimization Methods

机译:通过优化方法将参数模型拟合到点云

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摘要

Computer Aided Design (CAD) is a powerful tool for designing parametric geometry. However, many CAD models of current configurations are constructed in previous generations of CAD systems, which represent the configuration simply as a collection of surfaces instead of as a parametrized solid model. But since many modern analysis techniques take advantage of a parametrization, one often has to re-engineer the configuration into a parametric model. The objective here is to generate an efficient, robust, and accurate method for fitting parametric models to a cloud of points. The process uses a gradient-based optimization technique, which is applied to the whole cloud, without the need to segment or classify the points in the cloud a priori..;First, for the points associated with any component, a variant of the Levenberg-Marquardt gradient-based optimization method (ILM) is used to find the set of model parameters that minimizes the least-square errors between the model and the points. The efficiency of the ILM algorithm is greatly improved through the use of analytic geometric sensitivities and sparse matrix techniques. Second, for cases in which one does not know a priori the correspondences between points in the cloud and the geometry model's components, an efficient initialization and classification algorithm is introduced. While this technique works well once the configuration is close enough, it occasionally fails when the initial parametrized configuration is too far from the cloud of points. To circumvent this problem, the objective function is modified, which has yielded good results for all cases tested.;This technique is applied to a series of increasingly complex configurations. The final configuration represents a full transport aircraft configuration, with a wing, fuselage, empennage, and engines. Although only applied to aerospace applications, the technique is general enough to be applicable in any domain for which basic parametrized models are available.
机译:计算机辅助设计(CAD)是用于设计参数几何的强大工具。但是,许多当前配置的CAD模型都是在前几代CAD系统中构建的,这些模型将配置简单地表示为表面的集合,而不是参数化的实体模型。但是,由于许多现代分析技术都利用了参数化的优势,因此通常必须将配置重新设计为参数模型。此处的目的是生成一种高效,鲁棒和准确的方法,用于将参数模型拟合到点云中。该过程使用基于梯度的优化技术,该技术适用于整个云,而无需事先对云中的点进行分段或分类。.;首先,对于与任何组件相关的点,使用Levenberg的变体-基于Marquardt梯度的优化方法(ILM)用于查找模型参数集,以最小化模型与点之间的最小平方误差。通过使用解析几何敏感性和稀疏矩阵技术,ILM算法的效率大大提高。其次,对于事先不知道云中的点与几何模型的组件之间的对应关系的情况,引入了一种有效的初始化和分类算法。虽然一旦配置足够接近,该技术就会很好地起作用,但是当初始参数化的配置离点云太远时,它有时会失败。为了解决这个问题,修改了目标函数,在所有测试的情况下都取得了良好的结果。该技术应用于一系列日益复杂的配置。最终配置表示完整的运输飞机配置,其中包括机翼,机身,尾翼和发动机。尽管仅适用于航空航天应用,但该技术具有足够的通用性,可应用于具有基本参数化模型的任何领域。

著录项

  • 作者

    Jia, Pengcheng.;

  • 作者单位

    Syracuse University.;

  • 授予单位 Syracuse University.;
  • 学科 Mechanical engineering.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 278 p.
  • 总页数 278
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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