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.

[1]  Anikó Ekárt,et al.  Genetic algorithms in computer aided design , 2003, Comput. Aided Des..

[2]  Pavel Krsek,et al.  Range Image Registration Driven By A Hierarchy Of Surface Differential Features , 1998 .

[3]  Linda G. Shapiro,et al.  Surface Reconstruction and Display from Range and Color Data , 2000, Graph. Model..

[4]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[5]  Enrique Dunn,et al.  Hybrid evolutionary ridge regression approach for high-accurate corner extraction , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Robert B. Fisher,et al.  Parallel Evolutionary Registration of Range Data , 2002, Comput. Vis. Image Underst..

[7]  Kjell Brunnström,et al.  Genetic algorithms for free-form surface matching , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[8]  Emanuele Trucco,et al.  Robust motion and correspondence of noisy 3-D point sets with missing data , 1999, Pattern Recognit. Lett..

[9]  William H. Press,et al.  Numerical recipes in C , 2002 .

[10]  Jorma Rissanen,et al.  The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.

[11]  Aly A. Farag,et al.  A new genetic-based technique for matching 3-D curves and surfaces , 1999, Pattern Recognit..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Tong Lee,et al.  Surface registration using a dynamic genetic algorithm , 2004, Pattern Recognit..

[14]  Oscar Cordón,et al.  A CHC Evolutionary Algorithm for 3D Image Registration , 2003, IFSA.

[15]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[16]  Gustavo Olague,et al.  A new accurate and flexible model based multi-corner detector for measurement and recognition , 2005, Pattern Recognit. Lett..

[17]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[18]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Christian Roux,et al.  Registration of 3-D images by genetic optimization , 1995, Pattern Recognit. Lett..

[20]  Pavel Krsek,et al.  Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm , 2005, Image Vis. Comput..

[21]  Kim L. Boyer,et al.  Enhanced, robust genetic algorithms for multiview range image registration , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..