Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm

This paper reports on a successful application of genetic optimisation in 3D data registration. We consider the problem of Euclidean alignment of two arbitrarily oriented, partially overlapping surfaces represented by measured point sets contaminated by noise and outliers. Recently, we have proposed the Trimmed Iterative Closest Point algorithm (TrICP) [Chetverikov, D., Stepanov, D., Krsek, P., (2005). Robust Euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm. Image Vision Comput. 23, 299–309] which is fast, applicable to overlaps under 50% and robust to erroneous and incomplete measurements. However, like other iterative methods, TrICP only works with roughly pre-registered surfaces. In this study, we propose a genetic algorithm for pre-alignment of arbitrarily oriented surfaces. Precision and robustness of TrICP are combined with generality of genetic algorithms. This results in a precise and fully automatic 3D data alignment system that needs no manual pre-registration.