Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain
Keywords:
point cloud, simplification algorithm, particle swarm optimization (PSO), 3D laser scanning, large object, stored grainAbstract
The simplification of 3D laser scanning point cloud is an important step of surface reconstruction and volume estimation of bulk grain in granary. This study presented an adaptive simplification algorithm based on particle swarm optimization (PSO). It introduced PSO into the average distance method, a conventional simplification method. The basic idea of this algorithm was to adaptively determine the optimal point reducing intervals of scanning lines according to original point cloud density by PSO. By using the 3D point cloud scanned from bulk grain surface in granary, the proposed algorithm was validated. Compared with the average distance method, the proposed algorithm obtained more evenly distributed point set, smaller reduction ratio (6.96%) and higher volume estimation accuracy (relative error was less than 3‰). The 3D laser scanner (GSLS003, Jilin University and SkyViTech Co., Ltd., Hangzhou, China) used in this study could scan the complete picture of the grain surface in a granary in one time, so the acquired point cloud data do not have to be jointed. For the good simplification performance and capability of updating the reducing interval at any moment, the proposed algorithm and the 3D laser scanner could be used to realize online real-time measurement of stored bulk grain volume in granary. Keywords: point cloud, simplification algorithm, particle swarm optimization (PSO), 3D laser scanning, large object, stored grain DOI: 10.3965/j.ijabe.20160901.1805 Citation: Shao Q, Xu T, Yoshino T, Zhao Y, Yang W, Zhu H. Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain. Int J Agric & Biol Eng, 2016; 9(1): 71-78.References
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[2] Amenta N, Kil Y J. Defining point-set surfaces. ACM Transactions on Graphics, 2004; 23(3): 264–270. doi: 10.1145/1015706.1015713
[3] Levoy M, Pulli K, Curless B, Rusinkiewicz S, Koller D, Pereira L, et al. The digital michelangelo project: 3D scanning of large statues. Proceedings of SIGGRAPH, 2000.
[4] Liu H, Tao Y L, Fu J W. Data processing of scanning beam point-cloud based measuring freeform surface. Modular Machine Tool & Automatic Manufacturing Technique, 2011; (5): 77–80. (in Chinese with English abstract)
[5] Fang Y M, Xia Y H, Chen J. Study on point cloudy data simplification of goal based on improved angular deviation method. Journal of Earth Sciences and environment, 2012; 34(2): 106–110. (in Chinese with English abstract)
[6] Wang G F, Lü Y M, Han N, Zhang D. Simplification method and application of 3D laser scan point cloud date. Journal of Micro-nanolithography MEMS and MOEMS, 2012; 266–268. doi:10.2991/mems.2012.166
[7] Chen Z W, Da F P. 3D point cloud simplification algorithm based on fuzzy entropy iteration. Acta Optica Sinica, 2013; 33(8): 0815001-1–0815001-7. (in Chinese with English abstract)
[8] Mccafrey K J W, Rjones R, Holdsworth R E, Wison R W, Clegg P, Imber J, et al. Unlocking the spatial dimension: digital technologies and the future of geoscience fieldwork. Journal of the Geological Society, 2005; 162: 1–12. doi: 10.1144/0016-764905-017
[9] Qin J. Open access publishing of scientific scholarly journals in China. PhD dissertation. Tianjin: Tianjin University of Technology, 2011.63p. (in Chinese)
[10] Xu W H, Feng Z K, Su Z F, Xu H, Jiao Y Q, Deng O. An automatic extraction algorithm for individual tree crown projection area and volume based on 3D point cloud data. Spectroscopy and Spectral Analysis, 2014; 34(2): 465–471. (in Chinese with English abstract ).
[11] Zhu L L, Juha H. The use of airborne and mobile laser scanning for modeling railway environments in 3D. Remote Sensing, 2014; 6(4): 3075–3100. doi: 10.3390/ rs6043075
[12] Ferrari S, Ferrigno G, Piuri V. Reducing and filtering point clouds with enhanced vector quantization. IEEE Transactions on Neural Networks, 2007; 18(1): 161–176. doi: 10.1109/TNN.2006.886854
[13] Song H, Feng H Y. A global clustering approach to point cloud simplification with a specified data reduction ratio. Computer-Aided Design, 2008; 40(3): 281–292. doi: 10.1016/j.cad.2007.10.013
[14] Yu Z W, Wong H S, Peng H, Ma Q L. ASM: An adaptive simplification method for 3D point-based models. Computer-Aided Design, 2010; 42(7): 598–612. doi: 10.1016/j.cad.2010.03.003
[15] Shi B Q, Liang J, Liu Q. Adaptive simplification of point cloud using k-means clustering. Computer-Aided Design, 2011; 43(8): 910–922. doi: 10.1016/j.cad.2011.04.001
[16] Wang Y Q, Tao Y, Zhang H J, Sun S H. A simple point cloud data reduction method based on Akima spline interpolation for digital copying manufacture. International Journal of Advanced Manufacturing Technology, 2013; 69(9-12): 2149–2159. doi: 10.1007/s00170-013-5195-3
[17] Li K, Zhang A W. The Grain reserves quantity calculation method based on the laser point cloud. Bulletin of Surveying and Mapping, 2010; (supplement): 264–266. (in Chinese)
[18] Liang X H, Sun W D. A fast 3D surface reconstruction and volume estimation method for grain storage based on priori model. International Symposium on Photoelectronic Detection and Imaging, 2011; Vol.8192. doi: 10.1117/ 12.901036
[19] Ren G C, Yang Y, Guo H C. Intelligent grain storage measurement system design and research. FEMET, 2012; 430-432: 1881–1885. doi: 10.4028/www.scientific.net/ AMR.430-432.1881
[20] Pauly M, Gross M, Kobbelt L P. Efficient simplification of point-sampled surface. In: Visualization, IEEE. Boston, MA, USA. 2002; pp.163–170. doi: 10.1109/ VISUAL.2002.1183771
[21] Lu Z W, Wu J J, Sun F Y, Feng L M, He S J, Li W. Research and design of modern smart grain depot system. Journal of Henan University of Technology (Natural Science Edition), 2013; 34(5): 79–82. (in Chinese with English abstract)
[22] Qi G L, Wang W X, Yao G, Wang W J. The design of grain storage′s monitoring technology and system. Acta Agriculture Boreali-Occidentalis Sinica, 2006; 15(2): 167–169, 179. (in Chinese with English abstract)
[23] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE Int. Conf. Neural Network. Perth, Australia, 1995; 4: 1942–1948.
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Published
2016-01-31
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Qing, S., Tao, X., Tatsuo, Y., Yujie, Z., Wenting, Y., & Hang, Z. (2016). Point cloud simplification algorithm based on particle swarm optimization for online measurement of stored bulk grain. International Journal of Agricultural and Biological Engineering, 9(1), 71–78. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/1805
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Information Technology, Sensors and Control Systems
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