Robust and efficient GMM-based free-form parts registration via bi-directional distance

摘要

Compare to the ICP (Iterative Closet Points) registration method and its variants, the registration method based on GMM (Gaussian Mixture Models) is less sensitive to initial position, noise and outliers. For efficiency in a large-scale point sets alignment, the algorithm involved with FGT (Fast Gaussian Transformation) was proposed. However, due to its accuracy degeneration, the application of fast implementation is limited in large-scale point registration. Thus a modified GMM method is established to improve its accuracy and efficiency in point sets registration. To improve the precision and robustness of point density, noise and outliers, the corresponding weight matrix consisted of bidirectional gauss distance is proposed in this study. Instead of FGT (Fast Gaussian Transformation), the IFGT (Improved Fast Gaussian Transformation) and an adaptive adjustment based on axis-angle is proposed to further improve its efficiency and robustness about initial position simultaneously. We test capabilities of methods in classical model and symmetric and featureless manufacture parts. Compared to state-of-the-art methods in experiment, the result demonstrated applicability of proposed method in real life. (C) 2019 Published by Elsevier B.V.