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Left: 3D reconstruction of an object using the Matrox Imaging Library. Right: Laser triangulation setup using a camera from Sick IVP


to those known geometries,’ says Lina. A stereoscopic vision system guiding a robot handling a car door, for example, can use the features of the door to determine how to pick up the part. An object can also be imaged in 3D using


only one camera (monocular 3D), but only if everything about the geometry of the part is well-known. The dimensions and relative positions of a minimum of four or five features on the object must be known, according to Lina, to locate the part in 3D space with only one camera. ‘The challenge for stereoscopic imaging is to


calibrate the vision system as part of a robot cell,’ comments Boriero. ‘Not only does the camera have to be positioned with respect to the real world, but the camera has to be positioned with respect to the robot.’


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‘The advantage with stereoscopy is that it provides a 3D snapshot’


The advantage with stereoscopy is that it


provides a 3D snapshot; the object doesn’t need to be moving. However, according to Murhed of Sick IVP, resolution is often a limiting factor in stereovision, whereas laser triangulation generally provides higher resolution. ‘Laser triangulation provides a robust height map compared to stereoscopy, which requires high contrast to determine the height value,’ he says. ‘Stereovision is suited to robotic applications


in which the objects are large and it’s not critical how they are handled,’ Murhed states. Sick has developed, although not released, a laser triangulation system using a sweeping laser line for robot bin picking. By adding a prism


on the unit, the laser line can be moved over the bin to obtain a very accurate 3D height map of its contents. ‘This system is not dependent on the object’s contrast, as is the case with stereovision,’ he adds. According to Murhed, Sick has evaluated both stereovision and laser triangulation as potential solutions for random bin picking, concluding that the laser-based method is more effective.


3D overlaid with 2D Sick’s MultiScan technology combining 3D profile information with 2D features in the output from a single camera, is a further 3D imaging tool. ‘The 3D shape of an object is critical to


many applications, but 2D images are also important,’ comments Murhed. The company’s ColourRanger camera can provide RGB colour information along with the 3D elevation. Image processing can then be applied on a stack of six feature images, one containing 3D information, three containing RGB, and two looking at the level of light scatter, for example. The technology has been employed to grade


wood based on information about the shape of the wood, but also on surface defects like knots and stains. Scatter information can be combined with greyscale images, for instance, to distinguish between knots and stains. Using the information from the different feature images, it is possible to carry out an almost 100 per cent grading of the wood. ‘With any machine vision task, the key is to


capture good images that enhance the features of interest,’ comments Murhed. ‘One 2D image will always be a compromise and not all the features will be enhanced. The MultiScan approach allows a number of images to be captured, each enhancing different features.’


Image courtesy of Sick IVP.


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