A Chebyshev metamodel based BnB approach to efficiently search global optimum for 3D ICP point set registration

Abstract

Iterative Closest Point (ICP) algorithm for 3D point set registration has many robust properties that make it widely used. Regardless of the initialization, the Go-ICP method was first presented to search global optimum for 3D point set registration using Branch-and-Bound (BnB) scheme, but it could be time consuming. In this paper, an efficient modification, called Ego-ICP, is presented. The core idea is: (1) SE(3) feasible domain, L2-norm registration model of ICP and corresponding local ICP algorithm are reviewed, (2) Chebyshev metamodel is implemented to approximate the registration model with specific rotation, (3) the Chebyshev metamodel based single nested BnB approach is then presented to efficiently search global optimum for 3D point set registration. Furthermore, three experimental targets for different Datasets are demonstrated and shown that the presented approach, as compared with the BNB based method, the geometry based methods and the deep learning based method, (1) can be faster than or close to the efficiency of other compared methods, (2) can be reliable regardless of the initialization, and (3) can be 100% successful even if noise and outlier are existed. Lastly, several insights are provided to further improve the efficiency of our method.