Relative Position Algorithm for Optimal Camera Placement of Large Scale Volume Localization System

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Abstract:

Large Scale Volume Localization System (LSVLS) with camera network has appropriate precise and cost, which is a promising system in metrology and localization in industry and lives. Optimal camera placement is significant to lower cost and facilitate target’s auto-control for mobile robot in the large workspace. The author proposed a relative position algorithm (RPA) to find optimal camera placement of dozens even hundreds cameras. RPA calculated the minimum cameras and the coordinate and posture of each camera, after figured out the best posture of the camera in camera placement area. The result of optimal camera placement can enhance greatly the efficiency of camera placement in LSVLS and is verified with a model of a mobile robot works in a laboratory.

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1442-1446

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August 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Estler W T, Edmundson K L, Peggs G N, et al. Large-scale metrology–an update[J]. CIRP Annals-Manufacturing Technology, 2002, 51(2): 587-609.

DOI: 10.1016/s0007-8506(07)61702-8

Google Scholar

[2] Franceschini F, Galetto M, Maisano D, et al. Distributed large-scale dimensional metrology[M]. London: Springer, (2011).

DOI: 10.1007/978-0-85729-543-9_1

Google Scholar

[3] Zhou Hu. Study on the Vision-based Target Tracking and Spatial Coordinates Positioning System. Tianjin University. Ph.D. Dissertation, (2011).

Google Scholar

[4] Xiong Zhi. Research on network deployment optimization of workspace Measurement and Positioning System. Tianjin University. Ph.D. Dissertation, (2012).

Google Scholar

[5] Liang X, Sumi Y, Kim B K, et al. A large planar camera array for multiple automated guided vehicles localization[C]. Advanced Intelligent Mechatronics, 2008. AIM 2008. IEEE/ASME International Conference on. IEEE, 2008: 608-613.

DOI: 10.1109/aim.2008.4601729

Google Scholar

[6] Nikolaidis S, Arai T. Optimal arrangement of ceiling cameras for home service robots using genetic algorithms[C]. Robot and Human Interactive Communication, 2009. RO-MAN 2009. The 18th IEEE International Symposium on. IEEE, 2009: 573-580.

DOI: 10.1109/roman.2009.5326341

Google Scholar

[7] Xing Chen. Design of Many-Camera Tracking Systems for Scalability and Efficient Resource Allocation. Ph.D. Dissertation, Stanford University, June (2002).

Google Scholar

[8] Hu X, Eberhart R. Solving constrained nonlinear optimization problems with particle swarm optimization[C]. Proceedings of the sixth world multiconference on systemics, cybernetics and informatics. 2002, 5: 203-206.

Google Scholar

[9] Chakrabarty K, Iyengar S S, Qi H, et al. Grid coverage for surveillance and target location in distributed sensor networks[J]. Computers, IEEE Transactions on, 2002, 51(12): 1448-1453.

DOI: 10.1109/tc.2002.1146711

Google Scholar

[10] Ercan A O. Object tracking via a collaborative camera network. Ph.D. Dissertation, Stanford University, June (2007).

Google Scholar

[11] Horster E, Lienhart R. Approximating optimal visual sensor placement[C]. Multimedia and Expo, 2006 IEEE International Conference on. IEEE, 2006: 1257-1260.

DOI: 10.1109/icme.2006.262766

Google Scholar