Automatic outlier suppression for rigid coherent point drift algorithm

Abstract

Point pattern matching (PPM) including the hard assignment and soft assignment approaches has attracted much attention. The typical probability based method is Coherent Point Drift (CPD) algorithm, which treats one point set(named model point set) as centroids of Gaussian mixture model, and then fits it to the other(named target point set). It uses the expectation maximization (EM) framework, where the point correspondences and transformation parameters are updated alternately. But the anti-outlier performance of CPD is not robust enough as outliers have always been involved in operation until CPD converges. So we proposed an automatic outlier suppression mechanism (AOS) to overcome the shortages of CPD. Firstly, inliers or outliers are judged by converting matching probability matrix into doubly stochastic matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the model point set is forced to move coherently to target point set by this transformation model. The transformed model point set is imported into EM iteration again and the cycle repeats itself. The iteration finishes when matching probability matrix converges or the cardinality of accurate matching point set reaches maximum. Besides, the covariance should be updated by the newest position error before re-entering EM algorithm. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust and efficient. It offers a good practicability and accuracy in rigid PPM applications.