Fitting two concentric spheres to data by orthogonal distance regression

The problem of this research tackles the process of fitting two concentric spheres to data, which arises in computational metrology. There are also many fitting criteria that could be used effectively, and the most widely used one in metrology, for example, is that of the sum of squared minimal distance. However, a simple and robust algorithm assigned for using the orthogonal distance regression will be proposed in this paper. A common approach to this problem involves an iteration process which forces orthogonality to hold at every iteration and steps of Gauss-Newton type.