3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization (PSO) with iterative closest point (ICP) algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.
Quan Yu Kesheng Wang
. A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method[J]. Advances in Manufacturing, 2014
, 2(1)
: 32
-38
.
DOI: 10.1007/s40436-014-0059-0
1. Granero L, Sa´nchez J, Mico´ V, Esteve JJ, Herva´s J, Simo´n S,
Pe´rez E (2007) 3D digitising using structured illumination.
Application to mould redesign, vol 6616, Part 2 edn
2. Barbero BR, Ureta ES (2011) Comparative study of different
digitization techniques and their accuracy. Comput Aided Des
43(2):188–206
3. Rocchini C, Cignoni P, Montani C, Pingi P, Scopigno R (2001) A
low cost 3D scanner based on structured light. Eurographics
20:299–308
4. Besl PJ, McKay HD (1992) A method for alignment of 3-D
shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
5. Chen Y, Medioni G (1991) Object modeling by alignment of
multiple range images. In: Proceedings of IEEE international
conference on robotics and automation, vol 2723. pp 2724–2729
6. Zhang Z (1994) Iterative point matching for alignment of freeform
curves and surfaces. Int J Comput Vis 13(2):119–152
7. Delibasis K, Asvestas PA, Matsopoulos GK (2010) Multimodal
genetic algorithms-based algorithm for automatic point correspondence.
Pattern Recognit 43(12):4011–4027
8. Gold S, Rangarajan A, Lu C-P, Pappu S, Mjolsness E (1998) New
algorithms for 2D and 3D point matching: pose estimation and
correspondence. Pattern Recognit 31(8):1019–1031
9. Gruen A, Akca D (2005) Least squares 3D surface and curve
matching. ISPRS J Photogramm Remote Sens 59(3):151–174
10. Masuda T, Sakaue K, Yokoya N (1996) Alignment and integration
of multiple range images for 3-D model construction. In:
Proceedings of the 13th international conference on pattern recognition,
vol 1, 871 pp 879–883
11. Turk G, Levoy M (1994) Zippered polygon meshes from range
images. In: Proceedings of the 21st annual conference on computer
graphics and interactive techniques (ACM), pp 311–318
12. Pulli K (1999) Multiview alignment for large data sets. In: Proceedings
of second international conference on 3-D digital
imaging and modeling, pp 160–168
13. Simon DA (1996) Fast and accurate shape-based alignment.
Carnegie Mellon University, Pittsburgh
14. Godin G, Rioux M, Baribeau R (1994) Three-dimensional
alignment using range and intensity information, vol 2350
pp 279–290
15. Kennedy J, Eberhart R (1995) Particle swarm optimization. In:
Proceedings of IEEE international conference on neural networks,
vol 4, pp 1942–1948
