Advertisement

The Research on AGV Visual Guided Under Strong Noise

  • Xiaohong Zhang
  • Yifan Yang
  • Wanli Xing
  • Hong Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

In this paper, considering the complexity of AGV working environment, the image filtering, segmentation and graph morphology processing are carried out for the high noise of AGV working environment. To solve the problem of uneven light in the process of AGV travel, a dynamic threshold transfer and segmentation method is proposed, which greatly improves the accuracy and efficiency of the segmentation. At the same time, after extracting the morphological gradient of the marking line, the whole inspection method of image acquisition and the label processing of the boundary are established, and a one-sided fast center line searching algorithm is proposed. Compared with the traditional algorithm, the computation cost is reduced by more than 50%.

Keywords

Visual navigation AGV Strong noise Fast dynamic threshold Morphological filtering Center line extraction 

References

  1. 1.
    Zhao, K.: Research on visual navigation of intelligent patrol robot in the substation. School of Electrical and Electronic Engineering (2014)Google Scholar
  2. 2.
    Zhu, S., You, C.: A modified average filtering algorithm. Comput. Appl. Softw. 12, 97–99 (2013)Google Scholar
  3. 3.
    Zhang, X., Chen, S.: A neighborhood mean filter algorithm based on statistical features. SCI/TECH Inf. Dev. Econ. 15(2), 146–147 (2005)Google Scholar
  4. 4.
    Zhao, H.: Image denoising based on adaptive fuzzy weighting. Capital Normal University (2008)Google Scholar
  5. 5.
    Shi, H., He, Y.: Navigation algorithm of automated guided forklift based on STM32F103ZET6. Microcontroll. Embed. Syst. 16, 33–36 (2016)Google Scholar
  6. 6.
    Zhang, L., Duan, Z.: Mobile robot navigation algorithm based on embedded processor. Ind. Control Comput. 23(6), 65–66 (2010)Google Scholar
  7. 7.
    Zhu, F., Yu, F., Han, Y., Lei, Y., Hu, Y.: Path recognition algorithm for intelligent vehicle based on camera. J. Chin. Agric. Mech. 34(5), 202–206 (2013)Google Scholar
  8. 8.
    Wei, Z., et al.: Study on substation intelligent inspection robot based on visual navigation. Shaanxi Electr. Power 43(6), 63–66 (2015)Google Scholar
  9. 9.
    Huang, D., et al.: Adaptive weighted mean algorithm for uneven illuminated image. Sci. Technol. Guide 33(8), 84–88 (2015)Google Scholar
  10. 10.
    Wellner, P.D.: Adaptive thresholding for the DigitalDesk. Xerox (1993)Google Scholar
  11. 11.
    Chen, X., et al.: Adaptive threshold binarization and morphology image processing based on FPGA. Electron. Meas. Technol. 39(7), 67–71 (2016)Google Scholar
  12. 12.
    Lei, P.: Digital image processing of AGV based on vision navigation. Chin. J. Sci. Instrum. 27, 766–767 (2006)Google Scholar
  13. 13.
    Wu, H., Pan, Y.: Research on image processing algorithm for vision navigation intelligent vehicle path recognition. J. Southwest. Norm. Univ. (Nat. Sci. Edn.) 39(3), 108–115 (2014)MathSciNetGoogle Scholar
  14. 14.
    Isozaki, N., Chugo, D., Yokota, S., et al.: Camera-based AGV navigation system for indoor environment with occlusion condition. In: 2011 IEEE International Conference Mechatronics and Automation (2011)Google Scholar
  15. 15.
    Nie, R.C., Min, H.E., Zhou, D.M., et al.: Global threshold segmentation algorithm for visual form images. Laser Infrared 47(02), 234–238 (2017)Google Scholar
  16. 16.
    An, Q., Li, Z., Ji, C., et al.: Agricultural robot vision navigation algorithm based on illumination invariant image. Trans. CSAE 25(11), 208–212 (2009)Google Scholar
  17. 17.
    Xiao, Q.Z., Xu, K., Guan, Z.Q., et al.: Structuring elements selection in morphology filter. Comput. Eng. Appl. 42, 49–51 (2007)Google Scholar
  18. 18.
    Xu, S., Yang, X., Jiang, S.: A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Process. 131, 99–112 (2017)CrossRefGoogle Scholar
  19. 19.
    Zhu, Y., Wang, W.C., Wang, M.H.: Research on intelligent vehicle path recognition and tracking algorithm based on Electromagnetism. Sens. World 3, 008 (2014)Google Scholar
  20. 20.
    Jimenez, A.E., Dabrowski, A., Sonehara, N., Martinez, J.M.M., Echizen, I.: Tag detection for preventing unauthorized face image processing. In: Shi, Y.-Q., Kim, H.J., Pérez-González, F., Yang, C.-N. (eds.) IWDW 2014. LNCS, vol. 9023, pp. 513–524. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19321-2_39CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiaohong Zhang
    • 1
  • Yifan Yang
    • 2
  • Wanli Xing
    • 2
  • Hong Zhang
    • 2
  1. 1.Luoyang Institute of Electro-Optical DevicesLuoyangChina
  2. 2.Image Processing CenterBeihang UniversityBeijingChina

Personalised recommendations