Stereo vision

In stereo vision, 3D information about a scene can be extracted by comparing two images taken from different viewpoints. The main idea behind using a camera pair for measuring depth is the fact that object points appear at different positions in the two camera images depending on their distance from the camera pair. Very distant object points appear at approximately the same position in both images, whereas very close object points occupy different positions in the left and right camera image. The object points’ displacement in the two images is called disparity. The larger the disparity, the closer the object is to the camera. The principle is illustrated in Fig. 16.

_images/stereo_sketch_v2.svg

Fig. 16 Sketch of the stereo-vision principle: The more distant object (black) exhibits a smaller disparity d_2 than that of the close object (gray), d_1.

Stereo vision is a form of passive sensing, meaning that it emits neither light nor other signals to measure distances, but uses only light that the environment emits or reflects. Thus, the Balluff products utilizing this sensing principle can work indoors and outdoors and multiple devices can work together without interferences.

To compute the 3D information, the stereo matching algorithm must be able to find corresponding object points in the left and right camera images. For this, the algorithm requires texture, meaning changes in image intensity values due to patterns or the objects’ surface structure, in the images. Stereo matching is not possible for completely untextured regions, such as a flat white wall without any visible surface structure. The stereo matching method used by the rc_visard is SGM (Semi-Global Matching), which provides the best trade-off between runtime and accuracy, even for fine structures.

The following software modules are required to compute 3D information:

  • Camera: This module is responsible for capturing synchronized image pairs and transforming them into images approaching those taken by an ideal camera (rectification).
  • Stereo matching: This module computes disparities for the rectified stereo image pair using SGM.

For stereo matching, the position and orientation of the left and right cameras relative to each other has to be known with very high accuracy. This is achieved by calibration. The rc_visard’s cameras are pre-calibrated during production. However, if the rc_visard has been decalibrated, during transport for example, then the user has to recalibrate the stereo camera:

  • Camera calibration: This module enables the user to recalibrate the rc_visard’s stereo camera.