Engineering Surveys for Industry by Alojz Kopáčik & Ján Erdélyi & Peter Kyrinovič

Author:Alojz Kopáčik & Ján Erdélyi & Peter Kyrinovič
Language: eng
Format: epub
ISBN: 9783030483098
Publisher: Springer International Publishing

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In the case of feature-based registration, the transformation parameters are estimated on the base of the mathematical models of geometrical primitives such as planes, cylinders, spheres, or toruses. According to Rabbani et al. (2007), it is possible to distinguish between direct and indirect transformation methods. By the indirect transformation method, simple geometrical shapes are defined in point clouds, in which mathematical models are estimated using regression algorithms. The transformation parameters are then estimated by minimizing the sum of the squares of differences between the parameters of the mathematical models of particular geometrical shapes (e.g., identical cylinder in both point clouds). By the direct method, the parameters estimated by the indirect method are used as approximate values. The sum of squares of orthogonal distances between the points and their respective geometric features is minimized (e.g., the sum of squares of the orthogonal distances from a plane). The parameters of mathematical models of geometrical primitives and the transformation parameters are estimated in one step. The advantage of the feature-based registration is that, theoretically, no overlap between point clouds is necessary. It is enough if the modeled primitive appears in both point clouds (Lichti and Skaloud 2010). From a practical point of view, it is recommended to model the corresponding geometrical primitives from points lying on the same part of the surface to eliminate the errors from the imperfection of the geometrical shape of the modeled object (e.g., part of a pipe may not be an ideal cylinder).

After the registration of point clouds into a common coordinate system, it is necessary to realize other modifications. These adjustments can be divided into the removal of the outliers, removal of the redundant points, assigning a color to the point cloud, reduction of data, and conversion of the data to the required file format. Depending on the scanning conditions, often are scanned also structures not related to the measured object (Fig. 6.14). These can be objects in the close surroundings of the scanned object, e.g., transportation (especially if the object is scanned from a long distance), but it can also be people, animals (birds), dust, or snowflakes (or raindrops).

Fig. 6.14Point cloud of ship’s hull after registration (left) and point cloud after elimination of redundant points (right)

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