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presented approach while offering the already men-
tioned advantages. Despite the rather restricted low
level application example, the presented approach is
versatile and can be used in arbitrary context as long
quality metrics can be defined. In contrast to com-
monly used Kalman filters, the used models are not
restricted to be linear such that also high level percep-
tion with complex non-linear models is supported.
In future works, the quality concept will be ex-
tended to other perception processes. The huge range
of perception algorithms offers various applications
of the new perception network. Thereby, the impact
of quality metrics to the different algorithms will be
examined and standard based operations will be de-
fined. Furthermore, the concept should be used for
robot control. Different applications of the quality
data like influence on the robots navigation will be in-
vestigated. For example, the robot could navigate in a
more cautious way (keeping a larger distance to obsta-
cles or reducing speed) if there are uncertain data de-
tected. Another, topic is the usage of behavior meta-
signals as virtual sensors. By incorporating knowl-
edge about the systems intentions into the perception
system, more sophisticated cross evaluations could be
performed and the overall data quality raised.
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