Machine Vision is one of the driving forces of industrial automation. For a long time, it's been primarily pushed forward by improvements made in 2D image sensing, and for some applications, 2D sensing is still an optimal tool to solve a problem. But the majority of challenges machine vision is facing today has a 3D character. From a well-established metrology up to new applications in smart robotics, 3D sensors serve as a main source of data. Under a 3D sensor, we understand a sensor that is able to capture 3D features of inspected surface. While we are talking about the machine vision, we will not consider non-optical systems in this category.
Nowadays, market offers a wide variety of 3D sensoric solutions, most of them claiming a superiority over their competition. While a lot of these claims are based on a reational reasoning, one needs to understand differences and the need for individiual applications. FOr QR code reading, a 2D smart can be the best solution on market. But it will probably not guide a logistic robot from one facility to another. In this field, it can't compete with LIDAR based solutions currently dominating that market.
Not considering interferemetry and a nm range, we can list typical, most common technologies currently used in the industry:
- Laser triangulation
- Stereo vision
- Structured light