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Generation of Environmental Representation of a Large Indoor Parking Lot

  • Jung-Ming Wang
  • Chih-Fan Hsu
  • Sei-Wang Chen
  • Chiou-Shann Fuh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

A technique for generating environmental representations of indoor parking lots is presented. The environmental representation of a parking lot is of use for the surveillance and management of the parking lot. The representation includes a map and an image, called the environmental map and the environmental image, respectively. Given the ceiling and floor plans of an indoor parking lot, we first determine the locations for installing omni-directional (OD) cameras such that the integrated fields of view (FOV) of the cameras cover the entire parking lot. After installing cameras, those monitoring views collected from the various cameras are then dewarped and mosaiced together to form an image of the whole environment. To test our method, we constructed a monitoring system for a model of parking lot that is scaled from a real one.

Keywords

Covering problem omni-directional camera camera calibration path planning camera deployment environment map 

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References

  1. 1.
    Wang, J.M., Tsai, C.T., Chen, S., Chen, S.W.: Omni-Directional Camera Networks and Data Fusion for Vehicle Tracking in an Indoor Parking Lot. In: IEEE International Conference on Advanced Video and Signal based Surveillance, Sydney, p. 45 (2006)Google Scholar
  2. 2.
    Hert, S., Tiwari, S., Lumelsky, V.: A Terrain-Covering Algorithm for an AUV. Journal of Autonomous Robots 3, 91–119 (1996)CrossRefGoogle Scholar
  3. 3.
    Yilmaz, N.K., Evangelinos, C., Patrikalakis, N.M., Lermusiaux, P.F.J., Haley, P.J., Leslie, W.G., Robinson, A.R., Wang, D., Schmidt, H.: Path Planning Methods for Adaptive Sampling of Environmental and Acoustical Ocean Fields. J. Oceans, 1–6 (2006)Google Scholar
  4. 4.
    De Carvalho, R.N., Vidal, H.A., Vieira, P., Ribeiro, M.I.: Complete Coverage Path Planning and Guidance for Cleaning Robots. In: Proceedings of the IEEE International Symposium on Industrial Electronics, Guimaraes, Portugal, pp. 677–682 (1997)Google Scholar
  5. 5.
    Fu, Y., Lang, S.Y.T.: Fuzzy Logic Based Mobile Robot Area Filling with Vision System for Indoor Environments. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, Monterey, USA, pp. 326–331 (1999)Google Scholar
  6. 6.
    Luo, C., Yang, S.X., Meng, Q.-H.: Real-time Map Building and Area Coverage in Unknown Environments. In: Int’l Conf. on Roboticss and Automation, Barcelona, pp. 1736–1741 (2005)Google Scholar
  7. 7.
    Yang, S.X., Luo, C.: A Neural Network Approach to Complete Coverage Path Planning. J. Systems, Man, and Cybernetics-Part B: Cybernetics 34(1), 718–725 (2004)CrossRefGoogle Scholar
  8. 8.
    Lee, J.H., Choi, J.S., Lee, B.H., Lee, K.W.: Complete Coverage Path Planning for Cleaning Task using Multiple Robots. In: Proceedings of the 2009 IEEE Int’l Conf. on Systems, Man, and Cybernetics, San Antonio, pp. 3618–3622 (2009)Google Scholar
  9. 9.
    Janchiv, A., Batsaikhan, D., Kim, G.H., Lee, S.G.: Complete Coverage Path Planning for Multi-Robits Based on. In: Int’l Conf. on Control, Automation and System, pp. 824–827. KINTEX, Gyeonggi-do (2011)Google Scholar
  10. 10.
    Soro, S., Heinzelman, W.: A Survey of Visual Sensor Networks. J. Advances in Multimedia 2009, 1–22 (2009)CrossRefGoogle Scholar
  11. 11.
    Wang, Y.: On Full-view Coverage in Camera Sensor Networks. In: IEEE INFOCOM, Shanghai, pp. 1781–1789 (2011)Google Scholar
  12. 12.
    Hörster, E., Lienhart, R.: On the Optimal Placement of Multiple Visual Sensors. In: ACM Int’l Workshop on Video Surveillance and Sensor Networks, New York, pp. 111–120 (2006)Google Scholar
  13. 13.
    Huang, C.F., Tseng, Y.C.: The Coverage Problem in a Wireless Sensor Network. In: ACM Int’l Conf. on Wireless Sensor Networks and Application, New York, pp. 115–121 (2003)Google Scholar
  14. 14.
    Cardei, M., Wu, J.: Energy-Efficient Coverage Problems in Wireless Ad-hoc Sensor Networks. J. Computer Communication 29(4), 413–420 (2006)CrossRefGoogle Scholar
  15. 15.
    Grossberg, S.: Nonlinear Neural Networks: Principles, Mechanisms, and Architecture. J. Neural Networks 1, 17–61 (1988)CrossRefGoogle Scholar
  16. 16.
    Hodgkin, A.L., Huxley, A.F.: A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve. J. Physiology 117, 500–544 (1952)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jung-Ming Wang
    • 1
  • Chih-Fan Hsu
    • 2
  • Sei-Wang Chen
    • 2
  • Chiou-Shann Fuh
    • 1
  1. 1.CSIENational Taiwan UniversityTaipeiTaiwan
  2. 2.CSIENational Taiwan Normal UniversityTaipeiTaiwan

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