Matching based on variance minimization of component distances using edges of free-form surfaces

The basis for guidance in the field of automated robot processing is modeling by visual scanning. Matching algorithms are the real link that can be made between the ideal model and the subsequent robot processing. The matching algorithm that meets the machining requirements plays a pivotal role in the entire process, which provides the exact location of design models and measurement data. In order to meet the requirement of making the machining allowance uniform, a fine registration method which considers the variance minimization of the normal and tangential distances between the edge neighbors of the scattered point cloud is proposed. The edge neighbors are achieved by local growing of edge seeds based on the minimization of energy of supervoxel, which can represent the model more accurate than edge points extracted directly. The edge neighbor points are used to participate in the calculation, and the objective function of minimizing the variance of the two-way distance with the introduction of weight coefficients is proposed to constrain the iterative process. The effect of the distance in two directions on the result is analyzed, to determine the appropriate weight coefficients so that the matching calculation converges quickly and accurately. In comparison with other classical and state-of-the-art matching methods, the method in this paper performs well in terms of solution efficiency and accuracy of results. Moreover, the ability of this paper’s method to resist Gaussian noise is investigated, and it is found that this paper’s method has good robustness when σ is less than 1 for Gaussian noise. Ultimately, a uniformly distributed residual model is obtained to provide a visually guided basis for subsequent machining.