Uniform allowance model built on the ordered and disordered features of corresponding points

Registration is the basis for visual guidance in automated machining processes. This paper focuses on models which are with similar spatial structures. Using bounding boxes to represent outer contours, we extract sparse feature points from point clouds. In this process, matching results are critically affected by erroneous point pairs. Therefore, this paper introduces the Kullback-Leibler (K-L) divergence into the topography evaluation function. A sequential motion-invariant matrix is added to the function to describe the corresponding relationship. To even out the machining allowance, we propose a fine registration method. It considers minimizing variance in the allowance and tangent distance between corresponding points. Meanwhile, the judge criteria of similarity are proposed. They are based on the Hausdorff-Cosine similarity function. This function accounts for angles between neighboring point normals, reducing misidentification and ensuring correct counterparts are included in calculations. Compared with other algorithms, the method improves accuracy, speed of calculations, and ability to resist Gaussian noise. The resulting model ensures uniform allowance distribution. It's a visual prerequisite for further processing.