Robot vision eyes up German shows
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MVTec Software product story
Edited by the Engineeringtalk editorial team
Mar 28, 2006
Hannovermesse in Hanover and Automatica in Munich, Germany, to see machine vision tools for robotics for 3D measurement and 3D positioning.
During this year's Hannovermesse in Hanover and Automatica in Munich, Germany, MVTec Software will present machine vision tools for robotics.
The company claims that the connection of machine vision standard software to industrial robots is more efficient than proprietary development.
MVTec's Halcon software is for 3D machine vision in robotics for 3D measurement and 3D positioning.
If the setup is based on two cameras, two variable stereo techniques can be used.
The first is important for precise mould measuring.
It determines the depth of each object point based on transformed images via disparity (distance of corresponding pixels).
However, often it is sufficient to recognise only single points, angles or edges as selected features.
In addition to increased speed, this second technique can deliver sub-pixel or sub-millimeter accurate position data to the robot.
3D methods also exist for one-camera-setups.
After 3D camera calibration of the setup, Halcon reconstructs the object's 3D position in world co-ordinates (a method of monocular reconstruction) if the co-ordinates of the selected attributes of an object are known.
A similar method (3D position of cycles) makes use of circles in objects such as drill holes, for example, appearing in the camera image as ellipses.
By the analysis of these ellipses, the 3D position of the objects can be reconstructed.
Furthermore, a reconstruction is possible via depth by focus.
With the height adjustment of the camera, the 3D conditions of the object can be determined.
Photometric stereo acquires multiple images with illumination from different orientations.
The depth information is reconstructed by using the reflectance features of the object.
HALCON also provides all classical features for machine vision such as measuring, matching, OCR, data- and barcode reading, and blob analysis.
For in-situ quality control by robotics, these classical machine vision methods can be used for identification of components during bin picking in automotive engineering or for weld seam control.
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