A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

doi:10.1007/s40436-014-0059-0

Advances in Manufacturing

A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

Quan Yu • Kesheng Wang

Department of Production and Quality Engineering, Norwegian
University of Science and Technology, Trondheim, Norway

收稿日期:2014-01-14 出版日期:2014-03-21 发布日期:2014-01-27
通讯作者: e-mail: quan.yu@ntnu.no

A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

Quan Yu • Kesheng Wang

Department of Production and Quality Engineering, Norwegian
University of Science and Technology, Trondheim, Norway

Received:2014-01-14 Online:2014-03-21 Published:2014-01-27
Contact: e-mail: quan.yu@ntnu.no

摘要/Abstract

摘要： 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.

关键词: Quality inspection , Point cloud alignment , Particle swarm optimization (PSO) , Iterative closest point (ICP)

Abstract: 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.

Key words: Quality inspection , Point cloud alignment , Particle swarm optimization (PSO) , Iterative closest point (ICP)

Quan Yu Kesheng Wang. A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method[J]. Advances in Manufacturing, doi: 10.1007/s40436-014-0059-0.

参考文献

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

A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

A hybrid point cloud alignment method combining particle swarm optimization and iterative closest point method

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