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ABSOLUTE ROTATION ESTIMATION INCLUDING OUTLIER DETECTION VIA LOW-RANK AND SPARSE MATRIX DECOMPOSITION

机译:绝对旋转估计,包括通过低阶和稀疏矩阵分解进行的边缘检测

摘要

The present disclosure is directed to systems and methods to perform absolute rotation estimation including outlier detection via low-rank and sparse matrix decomposition. One example method includes obtaining a relative rotation estimates matrix that includes a plurality of relative rotation estimates. The method includes determining values for a low-rank matrix that result in a desirable value of a cost function that is based on a low-rank and sparse matrix decomposition of the relative rotation estimates matrix. The cost function includes the low-rank matrix and a sparse matrix that is nonzero in correspondence of one or more outliers of the plurality of relative rotation estimates. The method includes determining an absolute rotations matrix that includes a plurality of absolute rotations based at least in part on the values of the low-rank matrix that result in the desirable value of the cost function.
机译:本公开内容针对执行绝对旋转估计的系统和方法,包括经由低秩和稀疏矩阵分解的异常值检测。一种示例方法包括获得包括多个相对旋转估计的相对旋转估计矩阵。该方法包括基于相对旋转估计矩阵的低秩和稀疏矩阵分解来确定导致成本函数的期望值的低秩矩阵的值。成本函数包括低秩矩阵和与多个相对旋转估计中的一个或多个异常值相对应的非零的稀疏矩阵。该方法包括至少部分地基于导致成本函数的期望值的低秩矩阵的值来确定包括多个绝对旋转的绝对旋转矩阵。

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