Abstract
This chapter introduces a novel uncertainty model supporting visual depth perception for humanoid robots. The supervised learning approach models the visual uncertainty distribution using ground truth data. The validation and discussion of the attained model are presented.
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Notes
- 1.
The depth \(\delta _{Th}\) of a point in the scene produces a disparity smaller than a pixel.
- 2.
Since \(G^v\) is a 3\(\,\times \,\)3 matrix, the the singular values and vector coincide with the Eigenvalues and Eigenvectors.
- 3.
Higher orders \(n \ge 6\) were analyzed resulting insignificant deviations.
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González Aguirre, D.I. (2019). Visual Uncertainty Model of Depth Estimation. In: Visual Perception for Humanoid Robots. Cognitive Systems Monographs, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-97841-3_6
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