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7.2 Sensor Fusion and 3 D Object Pose Identification 101

)
a
viation (norm in s function of the noise level and the number of
sensors contributing to the desired output.

<∆(θ,Ψ,Φ)> 25

20

15

10
3
4
10 5
6
5 7 Number of Inputs
0 8
Noise [%]

Figure 7.4: The reconstruction deviation versus the number of fused sensory
inputs and the percentage of Gaussian noise added. By increasing the number of
fused sensory inputs the performance of the reconstruction can be improved. The
significance of this feature grows with the given noise level.

Fig. 7.4 exposes the results. Drawn is the mean norm of the orientation
angle deviation for varying added noise level from 0 to 10 % of the av-
8 fused sensory inputs, which were
erage image size, and for 3,4, and
taken into account. We clearly find with higher noise levels there is a grow-
ing benefit from an increasing increased number of contributing sensors.
And as one expects from a sensor fusion process, the overall precision
of the entire system is improved in the presence of noise. Remarkable
is how naturally the PSOM associative completion mechanism allows to
include available sensory information. Different feature sensors can also
be relatively weighted according to their overall accuracy as well as their
estimated confidence in the particular perceptual setting.