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A New Observer for Perspective Vision Systems With Partially Uncertain Linear Motion Parameters
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AI
DOI：10.1109/TSMC.2024.3411019.png)
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Abstract
En 中文
Depth estimation problem for the perspective vision systems has been extensively studied in the literature and the depth estimation can be achieved under the assumption that the exact camera motion parameters are available. However, in practice, it is hard to obtain the camera motion parameters exactly. For instance, the exact camera velocity is always unavailable and instead only the roughly estimated one can be obtained. This introduces the significant difficulties to achieve the depth estimation with partially uncertain camera motion parameters. In this article, we consider the depth estimation problem for the perspective vision system in the case that the camera angular velocities are accurate, while partial camera linear velocities are contaminated by some disturbances. This problem is theoretically formulated and solved for the first time in this article by proposing a new depth observer in the presence of the partially uncertain camera linear velocities. Local exponential convergence of the depth and disturbance estimates is achieved in presence of the partially uncertain camera linear velocities such that the estimation errors of states and disturbances converge to zero for constant disturbances or to a small error bound for bound time-varying disturbances. Simulations and experiments are carried out to verify the performance of the proposed observer.
Keywords:
Cameras
Estimation
Observers
Noise measurement
Accuracy
Machine vision
Convergence
Depth estimation
disturbance rejection
nonlinear observer
visual servo system
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W