In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts’ geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technical work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.
In recent years the requirements of industrial applications relating to image processing have significantly increased.
According to fast and modern production processes and optimized manufacturing of high quality products, new ways of
image acquisition and analysis are needed. Here the industrial computer tomography (CT) as a non-destructive
technology for 3D data generation meets this challenge by offering the possibility of complete inspection of complex
industrial parts with all outer and inner geometric features. Consequently CT technology is well suited for different kinds
of industrial image-based applications in the field of quality assurance like material testing or first article inspection.
Moreover surface reconstruction and reverse engineering applications will benefit from CT. In this paper our new
methods for efficient 3D CT-image processing are presented. This includes improved solutions for 3D surface
reconstruction, innovative approaches of CAD-based segmentation in the CT volume data and the automatic geometric
feature detection in complex parts. However the aspect of accuracy is essential in the field of metrology. In order to
enhance precision the CT sensor can be combined with other, more accurate sensor systems generating measure points
for CT data correction. All algorithms are applied to real data sets in order to demonstrate our tools.
As nowadays the industry aims at fast and high quality product development and manufacturing processes a modern and
efficient quality inspection is essential. Compared to conventional measurement technologies, industrial computer
tomography (CT) is a non-destructive technology for 3D-image data acquisition which helps to overcome their
disadvantages by offering the possibility to scan complex parts with all outer and inner geometric features. In this paper
new and optimized methods for 3D image processing, including innovative ways of surface reconstruction and automatic
geometric feature detection of complex components, are presented, especially our work of developing smart online data
processing and data handling methods, with an integrated intelligent online mesh reduction. Hereby the processing of
huge and high resolution data sets is guaranteed. Besides, new approaches for surface reconstruction and segmentation
based on statistical methods are demonstrated. On the extracted 3D point cloud or surface triangulation automated and
precise algorithms for geometric inspection are deployed. All algorithms are applied to different real data sets generated
by computer tomography in order to demonstrate the capabilities of the new tools. Since CT is an emerging technology
for non-destructive testing and inspection more and more industrial application fields will use and profit from this new
Segmentation and object recognition in point cloud are of topical interest for computer and machine vision. In this paper, we present a very robust and computationally efficient interactive procedure between segmentation, outlier detection, and model fitting in 3D-point cloud. For an accurate and reliable estimation of the model parameters, we apply the orthogonal distance fitting algorithms for implicit curves and surfaces, which minimize the square sum of the geometric (Euclidean) error distances. The model parameters are grouped and simultaneously estimated in terms of form, position, and rotation parameters, hence, providing a very advantageous algorithmic feature for applications, e.g., robot vision, motion analysis, and coordinate metrology. To achieve a high automation degree of the overall procedures of the segmentation and object recognition in point cloud, we utilize the properties of implicit features. We give an application example of the proposed procedure to a point cloud containing multiple objects taken by a laser radar.