  The efficient computation of viewpoints under consideration of various system
and process constraints is a common challenge that any robot vision system is
confronted with when trying to execute a vision task. Although fundamental
research has provided solid and sound solutions for tackling this problem, a
holistic framework that poses its formal description, considers the
heterogeneity of robot vision systems, and offers an integrated solution
remains unaddressed. Hence, this publication outlines the generation of
viewpoints as a geometrical problem and introduces a generalized theoretical
framework based on Feature-Based Constrained Spaces ($\mathcal{C}$-spaces) as
the backbone for solving it. A $\mathcal{C}$-space can be understood as the
topological space that a viewpoint constraint spans, where the sensor can be
positioned for acquiring a feature while fulfilling the regarded constraint.
The present study demonstrates that many viewpoint constraints can be
efficiently formulated as $\mathcal{C}$-spaces providing geometric,
deterministic, and closed solutions. The introduced $\mathcal{C}$-spaces are
characterized based on generic domain and viewpoint constraints models to ease
the transferability of the present framework to different applications and
robot vision systems. The effectiveness and efficiency of the concepts
introduced are verified on a simulation-based scenario and validated on a real
robot vision system comprising two different sensors.
