Laser line scanning provides a quick and effective way to inspect and reverse engineer complex parts and surfaces.
Converting Light into Points
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| The laser diode inside the unit produces a straight laser stripe projected onto a surface, and a camera observes the laser stripe at a known angle to determine the location for each point on the line. Near-field stripes are captured closest to the device and will have points closer together than far-field stripes. Standoff is the minimum distance required between the laser source and the scan object. Source: Faro Technologies Inc.
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Data is collected one slice, or cross section, at a time. A measurement arm acts as a referencing device—or localizer—that tracks and communicates to the host application software the position of each cross section in space. As the laser stripe is swept across an object, hundreds of cross sections are instantly captured. When they are collectively rendered in a CAD environment, the end result is a full 3-D digital representation of the object. The collection of raw data is commonly referred to as a point cloud.
Each of the captured cross sections contains hundreds of points. The number of points in each cross section depends on the size of the camera’s image sensor or charge-coupled device (CCD) and how much of the object is in the camera’s field of view. The maximum number of cross sections that can be acquired depends on the capture frame rate of the camera. A 30-hertz CCD can capture 30 frames, or stripes, per second.
The distance between points in a single stripe varies depending on the position within the field of view. Because of the angle of the camera, the field of view is not rectangular but rather trapezoidal. Stripes captured closest to the device, or in near field, will have points closer together than those captured farther away, or in far field. Standoff is the minimum distance required between the laser source and the scan object.
Higher frame rates and higher resolution CCDs can improve scanning speed and produce high-density point clouds capable of detecting finer details. While this is ideal to increase productivity and data quality, large amounts of scan data can quickly consume computer resources and reduce overall performance, requiring more expensive, high-power computers to get the job done.
To the camera, the laser stripe projected onto a part looks like a thick silhouette or profile. In order to produce a single row of points, the scanner must identify the pixels that run through the center of the profile. One data point corresponds to the position of one pixel in the CCD and each column produces one point on the profile.
The closer the laser line probe gets to the part (near field), the lower on the CCD the profile appears. As the laser scanner is pulled back (far field), the profile moves up on the CCD. The laser line silhouette is better defined in near field than in far field. A simple comparison would be to shine a flashlight on a wall; as the flashlight gets closer to the wall the light is brighter and the center spot is clearly defined, but as the flashlight is pulled back the center spot gets bigger and loses intensity and definition. This means that better accuracy and repeatability can be expected when holding the scanner closer to the part.
Sometimes surface properties such as color, texture and, in particular, reflectivity can diminish the quality of the image on the camera. This makes it more difficult for the laser probe to determine the true center of the profile, thus producing erroneous points that appear as noise in the data. Reflective surfaces can generate double images that typically result in outliers.
Noise in the data is almost inevitable and outliers are typically expected. There are many point cloud processing programs available that employ sophisticated and powerful algorithms to reduce noise and filter outliers.