Fig. 8

Intrinsic parameters are found by minimizing reprojection error of a known pattern in the image, either an artificial target (laboratory calibration, left column) or natural features (vicarious calibration, centre and right columns). In laboratory calibration, a chessboard is imaged from multiple orientations (A, B) and corners are identified by a pattern finding algorithm (red points). We know the geometry of the chessboard, so the points can be projected onto the image assuming camera position, orientation, and intrinsics. Solving for the camera position and orientation for each image, and intrinsic parameters common to all images, for many such images, gives us the intrinsics. The points from all the images must fill as much of the image as possible, and an example of all such points is shown here (C), color coded by the size of the residual reprojection error. Alternatively, features occuring naturally in the scene can be used, such as stars (D, E) , where they fill enough of the image (F). When stars do not fill enough of the image, a combination of points identified on simulated images of terrain (G) as well as stars (H) can be used to fill the field of view (I)