A novel algorithm is proposed in this paper to solve the optimal attitude determination formulation from vector observation pairs, that is, the Wahba problem. We propose here a fast analytic singular value decomposition (SVD) approach to obtain the optimal attitude matrix. The derivations and mandatory proofs are presented to clarify the theory and support its feasibility. Through simulation experiments, the proposed algorithm is validated. The results show that it maintains the same attitude determination accuracy and robustness with conventional methodologies but significantly reduces the computation time.
Accelerometer-magnetometer attitude determination is a common and vital medium processing technique in industrial robotics and consumer electronics. In this paper, we report a novel analytic attitude solution to the accelerometer-magnetometer combination in the sense of Wahba's problem. The Davenport matrix is analytically given and its eigenvalues are computed. Through derivations, the eigenvalues are simplified to very short expressions. Then, the corresponding eigenvectors are given accordingly via matrix row operations. The system is highly optimized based on the factorization and simplification of the obtained row-echelon form, which makes it computationally fast in practice. In this way, it is named as the fast accelerometermagnetometer combination (FAMC). Experiments on the correctness and advantages of the proposed solution are conducted. The results show that compared with conventional solutions, the proposed analytic solution is not only correct and accurate, but to our knowledge, the most time efficient as well.
The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity between the main visual elements is the most important indicator for detecting plagiarism of design concepts, which is important to protect cultural heritage and copyright. The purpose of this paper is to develop an efficient similarity evaluation scheme for graphic design. A novel deep visual saliency feature extraction generative adversarial network is proposed to deal with the problem of lack of training examples. It consists of two networks: one predicts visual a saliency feature map from an input image; the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Different from traditional saliency generative adversarial networks, a residual refinement module is connected after the encoding and decoding network. Design importance maps generated by professional designers are used to guide the network training. A saliency-based segmentation method is developed to not only locate the optimal layout regions but also notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show that the proposed model obtains state-of-the-art performance among various similarity measurement methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.