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.

翻译：在试图执行视觉任务时，任何机器人视觉系统都面临一个常见挑战：如何在考虑各种系统和过程约束的条件下高效计算视点。尽管基础研究已为这一问题提供了坚实可靠的解决方案，但一个能够提出形式化描述、考虑机器人视觉系统异构性并提供集成解决方案的整体框架仍有待解决。因此，本文将视点生成描述为几何问题，并引入了一个基于特征约束空间（$\mathcal{C}$-空间）的通用理论框架作为求解核心。$\mathcal{C}$-空间可理解为视点约束所张成的拓扑空间，在该空间中，传感器可被定位以获取特征，同时满足所考虑的约束。本研究证明，许多视点约束可高效地表述为$\mathcal{C}$-空间，并提供几何、确定性和封闭解。本文基于通用领域和视点约束模型对所引入的$\mathcal{C}$-空间进行表征，以促进所提框架在不同应用和机器人视觉系统间的可移植性。通过仿真场景验证了所引入概念的有效性和效率，并在包含两个不同传感器的真实机器人视觉系统上进行了实验验证。