Generally, a real-time stereo matching system employs a processor capable of implementing a stereo matching that represents a process for using a pair of two-dimensional images to obtain three-dimensional spatial information. In the real-time stereo matching system, if a scan line is equal to an epipolar line in each of two images in which two optical axes of a left and a right camera are parallel to each other, a pair of pixels which correspond to a point in the 3 dimensional space may be detected on a line of an image from the left camera (to be called a left line) and on a line of an image from the right camera (to be called right line).
A conventional processor for a fundamental of the stereo matching is disclosed in Uemsh R. Dhond and J. K. Aggarwal, Structure from Stereo—a review. IEEE Transactions on Systems, Man, and Cybernetics, 19(6): 553-572, November/December 1989. Further, a stereo matching technology for implementing the processor is disclosed in Jeong et al. (United States Patent Application Publication Number US2002/0025075 A1: Publication Date Feb. 28, 2002) “SYSTEM FOR MATCHING STEREO IMAGE IN REAL TIME”.
The conventional real-time stereo matching system disclosed in Jeong et al. includes a pair of cameras, wherein two cameras have same optical characteristics. If the pair of cameras observes a spatial area, similar spatial areas are captured in respective horizontal image scan lines of the pair of cameras. Therefore, a pixel in one digital image may be matched with another pixel of the other digital image, forming a pair, such that the pair of pixels corresponds to a point in a three-dimensional space.
Based on information on the pair of the pixels and simple geometrical characteristics, it is possible to calculate respective distances from the two cameras to a point in the three-dimensional space. In this case, a disparity indicates difference between an index of a pixel in one digital image captured by one camera and that of a corresponding pixel in the other digital image captured by the other camera, and a depth represents a geometrical distance calculated from the disparity. That is, the disparity may contain information on the distance. Thus, if three-dimensional information is derived from two digital images in real-time, it is possible to obtain information on a three-dimensional distance and shape of an observed space.
In other words, in case the pair of cameras of same optical characteristics observes a same spatial area, respective horizontal image scan lines of the left and the right camera correspond to similar spatial lines. Accordingly, a pixel in one digital image may be matched with another pixel in the other digital image and the pair of pixels corresponds to a point in the three-dimensional space so that respective distances from the cameras to a point in the three dimensional space can be calculated by using geometrical characteristics of the pixels in the digital images.
A disparity indicates a distance between a location of a pixel in one digital image and that of another pixel in the other digital image, and a depth represents geometrical characteristics calculated from the disparity. In other words, the disparity can represent distance information.
However, in order to carry out the above-described stereo matching process, a consistency of an inner factor of a camera, e.g., a focal distance, and a small distortion between camera lenses of the two cameras are required. Further, two cameras should be precisely fixed on desired locations by using precise optical devices, respectively. For this, the system should be provided with very precise cameras equipped with fine maneuverability to make a precise adjustment needed, resulting in an increase in a manufacturing cost of the system.
Meanwhile, the real-time stereo matching system can be employed to function as a visual device of a robot used in industries and home electronics and also as a road recognition device of an autonomous vehicle.
However, as described above, the conventional stereo matching system commands a high manufacturing cost because of the precise cameras and precise control devices needed to make, e.g., fine adjustments, making the system bulky.