Purpose
The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system.
Design/methodology/approach
A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets.
Findings
The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance.
Originality/value
Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.
Purpose
This paper aims to investigate a probabilistic mixture model for the nonrigid point set registration problem in the computer vision tasks. The equations to estimate the mixture model parameters and the constraint items are derived simultaneously in the proposed strategy.
Design/methodology/approach
The problem of point set registration is expressed as Laplace mixture model (LMM) instead of Gaussian mixture model. Three constraint items, namely, distance, the transformation and the correspondence, are introduced to improve the accuracy. The expectation-maximization (EM) algorithm is used to optimize the objection function and the transformation matrix and correspondence matrix are given concurrently.
Findings
Although amounts of the researchers study the nonrigid registration problem, the LMM is not considered for most of them. The nonrigid registration problem is considered in the LMM with the constraint items in this paper. Three experiments are performed to verify the effectiveness and robustness and demonstrate the validity.
Originality/value
The novel method to solve the nonrigid point set registration problem in the presence of the constraint items with EM algorithm is put forward in this work.
Accurate and Robust Non-rigid Point Set Registration using Student’s-t Mixture Model with Prior Probability Modeling
