Range Measurement Based On Binocular Stereo Vision

Stereovision, an active brunch of robotic technology, is one important attribute of intelligent robot. Binocular stereovision can be defined as the process of tri-dimensional reconstruction through the abstraction, identification, matching and interpretation of twain images gathered from identical objective, mostly include: video frequency trapping, camera calibration, image preprocessing, feature extraction, stereo matching and tri-dimensional reconstruction as well. It is through further research of binocular stereovision that we can bring important enlightenment to multi-eye vision system and present supporting technology for visual problem of intelligent locomotive robot, industry assembling robot, domestic robot and community servicing robot.Stereo vision is to reconstruct the three-dimensional coordinate of an object from some of its two-dimensional images. Generally, one would like to reconstruct the object in three-dimensional space with only two different view points. The fundamental principle of computer vision with dual-cameras have a strong resemblance to the visual perception procedure of mankind. We can get different images with two different view points and then calculate the position deviation of images pixels based on triangular measure. Finally we can reconstruct the three-dimensional coordinate of the object.The integrated binocular stereo vision system generally includes image acquisition,camera calibration,features extraction,stereo matching,object location and depth map construction,etc. The features extraction and the stereo matching is mainly discussed in this paper. First, we elaborate the fundamental principle of measure distance in stereo vision based on camera modeling. Second, the paper gives the elaborate analysis on preprocessing methods such as features extraction, and vrifies the algorithms by example. Furthermore, it summarizes the content of stereo matching such as match method, the strategy of match quality control. Finally, we submit the match algorithm based on image edge features and match algorithm based on the image interesting dot by features extraction and image matching.