| Volume 27 Issue 10 - Publication Date: 1 October 2008 |
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| Feature Depth Observation for Image-based Visual Servoing: Theory and Experiments |
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| A. De Luca, G. Oriolo, and P. Robuffo Giordano
Dipartimento di Informatica e Sistemistica,
Università di Roma “La Sapienza”,
Via Ariosto 25, 00185 Roma, Italy |
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| In the classical image-based visual servoing framework, error signals
are directly computed from image feature parameters, allowing, in
principle, control schemes to be obtained that need neither a complete
three-dimensional (3D) model of the scene nor a perfect camera calibration.
However, when the computation of control signals involves
the interaction matrix, the current value of some 3D parameters is required
for each considered feature, and typically a rough approximation
of this value is used. With reference to the case of a point feature,
for which the relevant 3D parameter is the depth Z, we propose a visual
servoing approach where Z is observed and made available for
servoing. This is achieved by interpreting depth as an unmeasurable
state with known dynamics, and by building a non-linear observer
that asymptotically recovers the actual value of Z for the selected
feature. A byproduct of our analysis is the rigorous characterization
of camera motions that actually allow such observation. Moreover, in
the case of a partially uncalibrated camera, it is possible to exploit
complementary camera motions in order to preliminarily estimate the
focal length without knowing Z. Simulations and experimental results
are presented for a mobile robot with an on-board camera in order
to illustrate the benefits of integrating the depth observation within
classical visual servoing schemes. |
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| Multimedia Key |
= Video |
 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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1 |
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Example
1: The video shows the external and camera
views of the experiment described in
Section 7.3, Figure 16(d). The depth observer
is initialized with a value of 8 m,
while the robot starts at about 4 m from
the target. 4.71MB (avi) |
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