The positioning method based on vision is one of the common ways in engineering application. So far, it is emphasized by many researchers, and will have a broad prospect in the future. The positioning approach based on traditional cameras has a narrow view, and it is hard to detect landmarks. As a result, the reliability is unstable. One imaging feature of fisheye vision is 180-degree wide field. So it can overcome the defects discussed above. This paper studies the fisheye calibration and indoor positioning based on the project supported by National Natural Science Foundation of China which is titled “Mobile robot motion estimation and its visual servo control based on fish eye vision”. This thesis mainly studies the design of calibration board used for the fisheye camera, the extraction of image characteristics, the fisheye lens calibration, the image correction and the positioning of fisheye camera based on multiple landmarks. The main contents of this paper are listed as follows: Firstly, a calibration board used for fisheye camera is designed, and the extraction of feature points is realized using this board. It lays the foundations for the fisheye lens calibration. For one thing, this board can adapt to the large radial distortion and ignore the tangential distortion so that the characteristics of landmarks can be kept. For another, the fisheye lens imaging area and ellipses are fitted in order to implement positioning. Then, the sorting of ellipses on the board is achieved. Secondly, the calibration of fisheye lens and the correction of fisheye images are obtained based on collected information. For one thing, the fisheye lens is calibrated using Kannala calibration algorithm and the Jacobian Matrix which is needed in Levenberg-Marquardt algorithm is derived. For another, calibrate fisheye lens using Kannala algorithm and Zhengyou Zhang algorithm respectively. Then, the correction of the fisheye image is achieved based on the parameters which are obtained from calibration. In addition, Matlab and C++ program are written in order to implement the processes above and the results are compared. Thirdly, on the basis of the work above, the positioning algorithm based on multiple landmarks is implemented using P4P solving method. Positioning experiments are carried in the conditions of arranging characteristic circles closer and farther in order to make full use of the large wide-angle of the fisheye lens. Then, the data are analyzed and com...
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