Optimal Camera Placement of Large Scale Volume Localization System for Mobile Robot

Abstract:

Large Scale Volume Localization System (LSVLS) is applied widely in industry. Large Scale Volume Localization System 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 optimized cameras placement with their relative position algorithm (RPA). The result of optimal camera placement enhances greatly the efficiency of camera placement in LSVLS and is verified with a model of field-winding mobile vehicle.

• Periodical: Advanced Materials Research (Volumes 945-949)
• Pages: 1390-1395
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.945-949.1390
• Online since: June 2014
• Authors: Ying Feng Wu, Gang Yan Li, Huan Yan
• Keywords: Camera Network, Large Scale Volume Localization System, Relative Position Algorithm

References

[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

[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

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

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

[5] 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

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

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

[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.

[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