The raising standards in manufacturing demands reliable and fast industrial quality control mechanisms. This paper proposes an accurate, yet easy to install multi-view, close range optical metrology system, which is suited to online operation. The system is composed of multiple static, locally overlapping cameras forming a network. Initially, these cameras are calibrated to obtain a global coordinate frame. During run-time, the measurements are performed via a novel geometry extraction techniques coupled with an elegant projective registration framework, where 3D to 2D fitting energies are minimized. Finally, a non-linear regression is carried out to compensate for the uncontrollable errors. We apply our pipeline to inspect various geometrical structures found on automobile parts. While presenting the implementation of an involved 3D metrology system, we also demonstrate that the resulting inspection is as accurate as 0.2 mm, repeatable and much faster, compared to the existing methods such as coordinate measurement machines (CMM) or ATOS.

In this paper, we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly.

We provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely localizable networks. We further study the computational complexity of network localization and investigate a subclass of grounded graphs where localization can be computed efficiently. We conclude with a discussion of localization in sensor networks where the sensors are placed randomly.