Pseudo Stereovision System (PSVS): A Monocular Mirror-based Stereovision System

For the autonomous movement of a robotic system, in real time, it has to perceive its environment, to calculate the position of a target or of a block and to move properly. For this reason, many types of sensors and apparatus have been proposed. The target state is required to be accurately calculated and a desired task or procedure to be safely and successfully integrated. In cases of limited intelligence of the system, mainly in industrial environments, the adaptation of the system to new data is necessary. The advantages of the proper use of sensors mounted on the end effector of a manipulator or on a mobile robot are multiple. Using cameras as sensors it is possible to have mainly vision systems with one or more cameras. A stereovision system is composed of two cameras. For the recovery of a 3-D scene from a pair of stereo images of the scene, it is required to establish correspondences. Correspondence between points in images is a major step in stereo depth perception. This step is known as the correspondence process, and many algorithms for it have been developed. Another major step is the computation of depth values from the point correspondences. The stereo process can be summarized by the following steps: 1) detection of features in each image 2) matching of features between images under certain geometric and other constraints and 3) calculation of depth using the disparity values and the geometric parameters of the imaging configuration. While each of these steps is important in the stereo process, the matching of features (correspondence between points) is generally thought to be the most difficult step and can easily become the most time consuming. Depth perception via stereo disparity is a passive method that does not require any special lighting or scanner to acquire the images. This method may be used to determine depths of points in indoor as well as outdoor scenes, and depths of points that are centimeters or kilometers away from the viewer. A correspondence algorithm can produce more reliable matches if the underlying images have smaller intensity and geometric difference. If the scene has Lambertian surfaces, there would be no difference in the intensities of corresponding points in images. For stereo images acquired by the two cameras, the focal lengths and zoom levels of the cameras are often slightly different. Differences in the optical properties of the two cameras cause intensity differences between corresponding points in stereo images. The optical axes of the