Research on point cloud to the complex surface best-fitting

An optimization method was presented for best-fitting of measurement point cloud to the complex surfaces, which consists of rough matching and accurate matching. The rough matching decided the variable range of the follow-up algorithms, whereupon the accurate matching acquired the optimal location. To cope with the accurate matching, based on a least-square method and L-BFGS-B algorithm, a new point to surface best-fitting method was put forward. For CMM data, this paper integrated a probe compensation method into the best-fitting process. The method has been compared to similar methods, such as Genetic algorithms, and is less subject to local optimization and has higher accuracy. This is demonstrated using examples at the end of the paper.