Abstract
This chapter explores how passivity can be utilized for visual feedback estimation. In particular, we present a real-time 3-D motion estimation mechanism called visual motion observer. Then, stability and tracking performance are analyzed by making use of passivity. The visual motion observer is also extended to a panoramic camera. Finally, the observer is extended to the case where an object motion model is available for motion estimation.
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- 1.
In the experiments of this book, apparent features are attached to the object, and they are extracted by an ad hoc program. Even in the practical case without such expedient features, the algorithms like KLT [23], SIFT [185], and SURF [29] would provide the features from some template images.
- 2.
For any matrices \(A \in {\mathbb R}^{n_1\times n_2}\) and \(B\in {\mathbb R}^{m_1\times m_2}\), the Kronecker product \(A\otimes B\) is defined by
where \(a_{\textit{ij}}\) is the \((i,j)\)-element of \(A\).
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© 2015 Springer International Publishing Switzerland
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Hatanaka, T., Chopra, N., Fujita, M., Spong, M.W. (2015). Passivity-Based Visual Feedback Estimation. In: Passivity-Based Control and Estimation in Networked Robotics. Communications and Control Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-15171-7_6
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DOI: https://doi.org/10.1007/978-3-319-15171-7_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15170-0
Online ISBN: 978-3-319-15171-7
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