Stochastic Joint Alignment of Multiple Point Clouds for Profiled Blades 3-D Reconstruction

Joint registration of point clouds obtained from multiple views is a key step of reconstruction for blades. However, due to the structural and surface characteristics of blades, some views do not meet the overlap constraints of registration, which results in significant initial errors of pose estimation. Thus, we propose a novel approach to recover the accuracy of poses estimation. The proposed method is robust to overlap extent of views through a stochastic framework. The approach formulates a variable-parameters graph optimization problem. Then a simulated annealing algorithm is used to solve the global optimal parameters. The candidate parameters in the simulated annealing process are obtained through the improved unscented Kalman filter, which reduces the initial errors and enhances the information matrices. The acceptance of the candidate parameters is determined by the optimization problem constrained by joint point correspondences and closed-loop consistency. And the parameters that can improve the registration accuracy are selected. We test our algorithm with simulated synthetic data and real data obtained by the robot measurement system. We compare the proposed algorithm with several state-of-the-art algorithms. The experimental results show that in the presence of significant initial errors, our method can estimate the poses more accurately and obtain better blade reconstructions.