Working principle of ccd visual positioning system

ccd visual positioning Due to the rapid development of digital image processing and computer vision skills, more and more researchers use cameras as the sensory sensor of fully autonomous mobile robots. This is mainly because the original ultrasonic or infrared sensor has a limited amount of information, and the visual system can make up for these shortcomings.

Camera calibration algorithm: Traditional camera calibration mainly includes Faugeras calibration method, Tscai two-step method, direct linear transformation method, Zhang Zhengyou plane calibration method and Weng iterative method. Self-calibration includes self-calibration method according to Kruppa equation, layered gradual self-calibration method, self-calibration method according to absolute quadric surface and Pollefeys' modulus binding method. Visual calibration includes Ma Songde's three orthogonal translation method, Li Hua's plane orthogonal calibration method and Hartley rotation method for internal parameter calibration. UV laser coding machine

ccd visual positioning algorithm: The positioning algorithm according to the filter mainly includes KF, SEIF, PF, EKF, UKF, etc. It is also possible to use a combination of monocular vision and odometer. Using the odometer reading as auxiliary information, triangulation is used to calculate the coordinate position of the feature point in the current robot coordinate system. The three-dimensional coordinate calculation here needs to be carried out on the basis of a delay of one time step. Two-axis automatic laser welding machine

The basic process of the positioning algorithm: the video stream obtained by the camera (mainly grayscale pictures, the pictures in stereo VO can be either colorful or gray), and the pictures obtained by the camera at t and t+1 are recorded as It And It+1, the camera's internal parameters, obtained after camera calibration, can be calculated as a fixed amount by matlab or opencv.

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