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• W2887105207 abstract "This paper addresses the problem of finding the closest generalized essential matrix from a given 6x6 matrix, with respect to the Frobenius norm. To the best of our knowledge, this nonlinear constrained optimization problem has not been addressed in the literature yet. Although it can be solved directly, it involves a large amount of constraints, and any optimization method to solve it will require much computational time. Then, we start by converting the original problem into a new one, involving only orthogonal constraints, and propose an efficient algorithm of steepest descent-type to find the solution. To test our algorithm, we start by evaluating our method with synthetic data, and conclude that the proposed method is much faster than applying general optimization techniques to the original problem with 33 constraints. To further motivate the relevance of our method, we apply our technique in two pose problems (relative and absolute) using synthetic and real data." @default.
• W2887105207 created "2018-08-22" @default.
• W2887105207 creator A5016015016 @default.
• W2887105207 creator A5087736280 @default.
• W2887105207 date "2017-09-19" @default.
• W2887105207 modified "2023-10-09" @default.
• W2887105207 title "Fitting Generalized Essential Matrices from Generic 6x6 Matrices with Applications to Pose Problems" @default.
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• W2887105207 hasPublicationYear "2017" @default.
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