Efficient registration of 2D points to CAD models for real-time applications

Efficient registration is a major challenge for real-time machine vision applications. Modern acquisition hardware can produce data at extremely high rates. Thus, efficient registration algorithms are required to align data to reference models to detect deviations and take correcting actions if needed. In this work, an efficient registration procedure of 2D points to CAD (computer-aided design) models is proposed. Recent developments in the field are reviewed and evaluated in terms of accuracy, speed and robustness. Efficient algorithms are proposed for the most computationally expensive parts of the registration, including an estimation of the rigid transform, a calculation of the closest point to geometric primitives, and an estimation of the surface normal. Furthermore, a novel primitive caching procedure is proposed that, when combined with an R-tree, greatly improves the execution speed of the registration. The result is a very accurate registration procedure, since geometric primitives are treated analytically with no point sampling required. At the same time, the proposed procedure is robust, very fast, and can achieve the correct registration in less than one millisecond.

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