首页> 外文学位 >Robust methods for Kalman filtering/smoothing and bundle adjustment.
【24h】

Robust methods for Kalman filtering/smoothing and bundle adjustment.

机译:卡尔曼滤波/平滑和束调整的鲁棒方法。

获取原文
获取原文并翻译 | 示例

摘要

Kalman filters and smoothers form an important class of algorithms used for inference on noisy dynamical systems, and are an industry standard in a wide range of applications, including space exploration, missile guidance systems, general tracking and navigation, and weather prediction. A classical topic in control theory, statistics, and signal processing, Kalman filtering methods can also be studied using optimization techniques, and this approach has led to efficient and accurate algorithms for nonlinear systems and models with inequality constraints.;We build on optimization and statistical perspectives to develop a range of new applications and algorithms, including smoothers robust to measurement errors, smoothers for systems with singular covariance, trend smoothers, and smoothers with state-dependent covariance models. We provide global convergence theory for these algorithms, and we exploit linear algebraic structure in the applications to ensure that the computational effort scales linearly with the number of time points.;We use a similar approach to develop a robust Bundle Adjustment algorithm, which is a well known method for visual reconstruction currently used by NASA Ames to make Digital Elevation Models (DEM). The new algorithm is robust against mistakes in feature matching, and exploits sparse linear algebraic structure in the application to keep the problem computationally tractable.
机译:卡尔曼滤波器和平滑器构成了用于推论嘈杂动力系统的重要算法类别,并且是包括太空探索,导弹制导系统,一般跟踪和导航以及天气预报在内的广泛应用中的行业标准。卡尔曼滤波方法是控制理论,统计和信号处理中的经典话题,也可以使用优化技术进行研究,这种方法可以为具有不等式约束的非线性系统和模型提供高效,准确的算法。开发一系列新应用和算法的观点,包括对测量误差具有鲁棒性的平滑器,具有奇异协方差的系统的平滑器,趋势平滑器以及与状态相关的协方差模型的平滑器。我们为这些算法提供全局收敛理论,并在应用程序中利用线性代数结构以确保计算工作量随时间点的数量线性增长。;我们使用类似的方法来开发鲁棒的Bundle Adjustment算法,这是一种NASA Ames当前用于制作数字高程模型(DEM)的一种众所周知的视觉重建方法。新算法对特征匹配中的错误具有鲁棒性,并在应用程序中利用稀疏线性代数结构来使问题在计算上易于处理。

著录项

  • 作者

    Aravkin, Aleksandr.;

  • 作者单位

    University of Washington.;

  • 授予单位 University of Washington.;
  • 学科 Applied Mathematics.;Statistics.;Mathematics.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 201 p.
  • 总页数 201
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:37:01

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号