Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

A point cloud registration algorithm based on Gaussian mixture model and point-to-plane metric was proposed to solve the problem of inaccuracy of object positioning under nonlinear illumination,clutter or occlusion environment.Firstly,assuming that elements of template cloud were generated by the Gaussian mixture model,the probability values of the components in Gaussian mixture model were allocated according to the point-to-plane distance.Further,a negative logarithmic likelihood function was constructed.Next,the EM algorithm was used to optimize the likelihood function,and the closed solution of Q function in maximum step was derived to improve the real-time performance of the algorithm.Using synthetic data and flange parts to test the proposed algorithm,the results showed that both the accuracy and robustness of the algorithm were superior to the traditional registration algorithm,and could meet the requirements of precise positioning in complex conditions.