Despite the proven advantages of 3D machine vision technology for inline quality inspection applications, many organizations still rely solely on 2D machine vision for their quality control processes.
While useful in a select number of scenarios, 2D vision is limited in its ability to achieve 100% quality control—which is why it is so important for organizations to invest in a smart 3D solution. To illustrate this point let's compare the basic capabilities of a 3D laser profiler to a 2D vision sensor.
3D Shape Measurement
A commonly overlooked problem is that 2D sensors do not support measurement related to 3D shape. As a result they aren’t able to measure critical features such as object flatness, surface angles, or part volumes. The inability to measure 3D shape is a major disadvantage of 2D considering most organizations need to inspect every aspect of the target.
On the other hand, a 3D laser profiler produces 3D shape information in the form of profiles or surfaces. These profiles provide critical information for determining if a part meets key tolerances for surface quality, assembly, fit and finish.
Because 2D sensors cannot detect depth, they are highly dependent on the distance from the camera to the target. This means 2D sensors have to be precisely fixtured at a fixed distance from the target, and require the use of scale-invariant feature detection or large telecentric optics (that must match the size of the sensor’s FOV) to counteract motion effects along the optical axis (ie., distance from the camera).
It’s a different story with 3D. The depth measurement information provided by 3D laser profilers eliminates errors due to object movement. This means objects can move anywhere within the sensor’s measurement range and still yield accurate results—effectively eliminating object fixturing requirements and improving overall measurement reliability.
2D sensors measure an object’s contrast (edge data), which means 2D relies on lighting and color/greyscale variation to detect features. This is especially problematic when inspecting low contrast objects where key features are the same color as the background. For example, 2D fails to measure a black object on a black background or distinguish part features without specific lighting to expose the presence and definition of an edge.
Unlike 2D intensity imaging 3D is contrast invariant. This means shape is measured regardless of surface color, which makes 3D ideal for measuring low contrast objects. And with 3D you don’t have to worry about ambient lighting or shadows affecting your scan results.
Application examples include:
• Scanning random parts
• Scanning for a range of product colors
• Scanning packaging with changing images/photos/text
• Isolating a scan of an object from a busy background
Robotic Inspection Applications
Industrial robots work in a three dimensional world. Unfortunately, 2D technology is unable to provide the necessary depth and spatial information (in 6 degrees of freedom) for the growing number of vision-guided robotics (VGR) systems used for automated quality control.
A 3D laser profiler (as well as a snapshot sensor) provides vision to a robot, allowing it to sense variations in its physical environment and adapt accordingly. This increases the robot’s flexibility, utility and speed in essential applications such as pick-and-place. On top of vision-guidance, laser profilers also provide built-in scan, measurement, and control features required for a wide range of flexible robotic inspection applications.
Combining 2D and 3D
Laser profilers combine 3D and 2D capability for total quality inspection. In addition to 3D shape measurements, the intensity of the projected laser or LED light is used to create a 2D image of the surface of a part. This information can be used to extract surface markings like bar codes and printed text.
For more information on extracting calibrated 2D intensity images from a 3D laser sensor, read this blog. You can also learn more about the benefits of combining 2D and 3D in this complimentary white paper.