Viewpoint Planning for Range Sensors Using Feature Cluster Constrained Spaces for Robot Vision Systems

The efficient computation of viewpoints for solving vision tasks comprising multi-features (regions of interest) represents a common challenge that any robot vision system (RVS) using range sensors faces. The characterization of valid and robust viewpoints is even more complex within real applications that require the consideration of various system constraints and model uncertainties. Hence, to address some of the challenges, our previous work outlined the computation of valid viewpoints as a geometrical problem and proposed feature-based constrained spaces ([Formula: see text]-spaces) to tackle this problem efficiently for acquiring one feature. The present paper extends the concept of [Formula: see text]-spaces to consider multi-feature problems using feature cluster constrained spaces ([Formula: see text]-spaces). A [Formula: see text]-space represents a closed-form, geometrical solution that provides an infinite set of valid viewpoints for acquiring a cluster of features satisfying diverse viewpoint constraints. Furthermore, the current study outlines a generic viewpoint planning strategy based on [Formula: see text]-spaces for solving vision tasks comprising multi-feature scenarios effectively and efficiently. The applicability of the proposed framework is validated on two different industrial vision systems used for dimensional metrology tasks.