Robust Point Cloud Registration Algorithm for Taylor Series Criterion Function

In order to reduce the influence of outliers on point sets registration and avoid the local minimum in iteration, Taylor series criterion function based robust point sets registration algorithm is proposed based on the robust criterion function point sets registration framework. The method includes the Taylor series criterion function and the determination of the initial value of registration. Firstly, to improve registration accuracy in the present of outliers, Taylor series criterion function by Taylor series expansion for Cauchy criterion function is proposed to limit the influence of outliers. Secondly, by calculating the center of gravity of the sets data, the initial translation vector is gotten with the difference of the model point sets and the data point sets. With this, the local minimum value issue of the iteration is addressed. Numerical experiments demonstrate the performance of Taylor series criterion function has been improved greatly in accuracy and stability compared with least squares minimization, Huber-criterion function, Tukey-criterion function and Cauchy-criterion function. The interpolation introduced to deal with the point sets without the homonymy point also helps to improve the accuracy of the subsequent registration.