Least-squares fitting for deformable superquadric model based on orthogonal distance

摘要

The least-squares fitting minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined as the orthogonal, or shortest distances from the given points to the geometric model to be fitted. Then the nonlinear optimization algorithm can be used to obtain the optimum solution. In this paper, we propose a geometric fitting algorithm for the deformable superquadric model, which is the computation of a measure of vector from each given point to orthogonal contacting point on the superquadric model, and estimates the optimum parameters of the model to minimize the squares sum of error distances. The estimated parameters by the proposed algorithm are invariant to coordinate transformation and we can easily find a physical interpretation of the fitting parameters.