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Note also that theoretically in this case L 1
u . This nice property can advan-
tageously be exploited to compute the control vector.
In practice, estimated camera parameters are used. The estimated rotation pa-
rameter θ
ω θ
u =
θ
u can be written as a nonlinear function of the real ones
ψ
(
θ
u ).Since
L 1
ω θ
u = θ
u , the closed-loop equation of the rotation control is
θ
u
dt
=
λ
L ω ψ
(
θ
u )
.
The asymptotic stability of this system has been studied for conventional camera
(
has a simple analytical form [16]. How-
ever, the stability analysis remains an open problem when
ξ
= 0) since in this case the function
ψ
ξ
= 0 since the nonlinear
function
ψ
is much more complex in this case.
16.3.3.2
2 1/2 D Visual Servoing
2 1/2 D visual servoing has been first proposed by Malis and Chaumette in case of
conventional camera (
= 0). In this section, the original scheme is extended to the
entire class of central cameras. In order to control the translational motion, let us
define s as
ξ
s = s 1 s 2
where s 1 =[ xy ] and s 2 = log(
) are respectively the coordinates of an image point
and the logarithm of the norm of its corresponding 3D point. The error between the
current value log(
ρ
ρ ) can be estimated using (16.8)
ρ
) and the desired value log(
s 2 = log(
since s 2
).
The corresponding interaction matrix L s can be written as
L s = J s 1 J s 2 L X
σ
where the Jacobian matrix J s 1
is given by (16.11), and J s 2
can be easily computed:
ρ 2 X .
J s 2 =
L s = L s v
L s ω can be obtained by stacking the interaction matrix in (16.11) and
000
1
σρ
2 ( x 2 + y 2 )
Φ ξ
1
L s 2 = J s 2 L X =
Φ
Φ
,
(16.12)
x
y
1+γξ
Z +
ξρ
ρ
1+
γξ
ρ can be estimated only
once during an off-line learning stage. If the system is supposed correctly calibrated
and that measurements are noiseless, then the control law is asymptotically stable
for any positive value ρ . However, the robustness with respect to calibration and
measurement errors still remains an open problem.
( x 2 + y 2 ) .
with
Φ
=
=
Note that the parameter
γ
+
ξ
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