Skip to main content
SpringerLink
Log in

Obstacles detection and depth estimation from monocular vision for inspection robot of high voltage transmission line

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Obstacles detection and distance estimation play an important role in vision navigation for inspection robot for high-voltage transmission lines which walks along the overhead ground wire. In view of images from inspection site, Harris corners matching is used to detect background motion caused by camera jitter to eliminate the motion through motion compensation and a method for selecting the effective matching point pairs is proposed in the paper. Then the frame difference image and binary image are processed together and complete moving objects are segment. On the basis of the obstacles detection an algorithm for distance estimation of them is put forward. The relation between the distance to be estimated and coordinate difference of both edges of the ground wire in image where the objects lie is obtained, so the distance can be acquired, by use of the pose of the camera relative to the wire, as well as the pin-hole imaging model. Experiments for distance estimation show that the method can achieve the estimation precision with less than 5% error within 500 cm, and it has many advantages such as easy implementation, fast processing speed, high estimation accuracy and robustness, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26

Similar content being viewed by others

References

  1. Katrasink, J., Pernus, F., Likar, B.: A survey of mobile robots for distribution power line inspection. IEEE Trans. Power Deliv. 25(1), 485–493 (2009)

    Article  Google Scholar 

  2. Huang, X., Ruan, Y., Li, Z., et al.: Research on 3D line identification for 500 kV extra-high voltage overhead power line inspection robot. Mach. Tool Hydraul. 39(11), 36–39 (2011)

    Google Scholar 

  3. Xu, X., Li, W., Wu, G., et al.: An autonomous inspection robot for transmission line along overhead ground line and its application. Eng. J. Wuhan Univ. 43(6), 752–756 (2012)

    Google Scholar 

  4. Wu, G., Xiao, X., Xiao, H., et al.: Development of a mobile inspection robot for high voltage power transmission line. Autom. Electric Power Syst. 30(13), 90–93 (2006)

    Google Scholar 

  5. Toth, J., Pouliot, N., Montambault, S.,: Field experiences using LineScout Technology on large BC transmission crossings. In: 1st International Conference on Applied Robotics for the Power Industry, IEEE. Piscataway, NJ, pp. 1–6 (2010)

  6. Wang, H., Jiang, Y., Liu, A., et al.: Research of power transmission line maintenance robots. In: SIACAS 1st International Conference on Applied Robotics for the Power Industry, IEEE. Piscataway, NJ, pp. 1–7 (2010)

  7. Wenming, C., Yaonan, W., Feng, Y., et al.: Research on obstacle recognition based on vision for deicing robot on high voltage transmission line. Chin. J. Sci. Instrum. 32(9), 2049–2056 (2011)

    Google Scholar 

  8. Wang, W., Wu, G., Bai, Y., et al.: Hand-eye-vision based control for an inspection robot’s autonomous line grasping. J. Cent. South Univ. 21(6), 2216–2227 (2014)

    Article  Google Scholar 

  9. Gang, Y., Yong, L., Fangmin, D., et al.: An obstacle detection approach of transmission lines based on contour view synthesis. In: Proceedings of the 2010 IEEE International Conference on Automation and Logistics, pp. 19–24 (2010)

  10. Hu, C., Wu, G., Cao, H,, et al.: Obstacle Recognition and Localization based on the Monocular Vision for Double Split Transmission Lines Inspection Robot, pp. 1–5 (2009)

  11. Fu, S., Liang, Z., Hou, Z., et al.: Vision based navigation for power transmission line inspection robot. In: IEEE International Conference on Cognitive Informatics IEEE Xplore, pp. 411–417 (2008)

  12. Zuo, Q., Xie, Z., Guo, Z.: Vision based obstacle recognition approach of a power line inspection robot. In: International Conference on Computational Intelligence and Natural Computing, pp. 459–462 (2009)

  13. Jin, L.-J., Yan, S., Liu, L.: Vibration damper recognition based on Haar-like features and cascade AdaBoost classifier. J. Syst. Simul. 24(9), 1086–1089 (2012)

    Google Scholar 

  14. Zuo, Q., Wang, Y.: An effective method of detection obstacles under a constraint. Metall. Autom. S1, 677–681 (2009)

    Google Scholar 

  15. Zhang, Y.: Structure-constrained obstacle recognition for transmission line inspection robot. Robot 29(1), 1–6 (2007)

    Google Scholar 

  16. Hu, C., Gongping, W.: Research of obstacle recognition based on vision for high voltage transmission line inspection robot. Chin. J. Sensors Actuators 21(12), 2092–2096 (2008)

    Google Scholar 

  17. Quanmin, L.: Virtual navigation for power transmission line inspection robot. Comput. Eng. Appl. 43(19), 221–224 (2007)

    Google Scholar 

  18. Liu, G.: Application study of image processing technology applied in vision system of inspection robot on power transmission lines. Comput. Eng. Des. 30(1), 36–40 (2009)

    Google Scholar 

  19. Zhan, C.: Obstacle recognition for transmission line inspection robot based on adaboost. Comput. Digit. Eng. 37(11), 130–133 (2009)

    Google Scholar 

  20. Wang, Y.: Bifocus imaging for monocular stereo vision. Opt. Tech. 33(6), 935–937 (2007)

  21. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  22. Wu, G., Tang, Z.: Distance measurement in visual navigation of monocular autonomous robots. ROBOT 32(6), 828–832 (2010)

    Google Scholar 

  23. Y, H., Zhang, W.: Method of vehicle distance measurement for following car based on monocular vision. J. Southeast Univ. 42(3), 542–546 (2012)

    Google Scholar 

  24. Zhan, Q., Huang, S., Wu, J.: Automatic navigation for a mobile robot with monocular vision. In: IEEE Conference on Robotics, Automation and Mechatronics, pp. 1005–1010 (2008)

  25. Chen, R., He, Z., Xiao, B.: Self-localization of mobile robot based on monocular and extended Kalman filter. In: The Ninth International Conference on Electronic Measurement & Instruments, pp. 450–454 (2009)

  26. Lin, X., Wei, H.: The depth estimate of interesting points from monocular vision. In: AICI, pp. 190–195 (2009)

  27. Zhao, H., Chen, X., Wang, J., et al.: Obstacle avoidance algorithm based on monocular vision for quad-rotor helicopter. Opt. Precis. Eng. 22(8), 2232–2241 (2014)

    Article  Google Scholar 

  28. Cao, W.: Research on Visual Control Methods for High Voltage Transmission Line Deicing Robot. Hunan university, Changsha (2013)

  29. Sun, P)., Zhang, Q., Li, W., et al.: Monocular multi-angle measurement method for spatial point. Chin. J. Sci. Instrum. 35(12), 2801–2807 (2014)

    Google Scholar 

  30. Huang, X., Gao, Feng, Xu, G., et al.: Depth information extraction of on-board monocular vision based on a single vertical target image. J. Beijing Univ. Aeronaut. Astronaut. 41(4), 649–655 (2015)

    Google Scholar 

  31. Meng, S., Wei, K., Bo, T.: Design of Overhead Transmission Lines. China Electric Power Press, Beijing (2015)

    Google Scholar 

  32. Xue, W.: The Research of Overhead Cable Theory About Forestry Cableway. Northeast Forestry University (2001)

  33. Zhenlu, Z., Benxian, C.: A study on the catenary-segment method for the analysis of flexible suspending cables. Eng. Mech. 7(4), 41–49 (1990)

    Google Scholar 

  34. Qu, J., Xin, Y.: Combined continuous frame difference with background difference method for moving object detection. Acta Photonica Sin 43(7), 1–8 (2014)

    Google Scholar 

  35. Ye, F., Xu, L.: Real-time detection and discrimination of static objects and ghosts. J. Zhejiang Univ. 49(1), 181–185, 192 (2015)

  36. Luo, J.: Application and implementation of motion estimation in image stabilization and matching tracking. National University of Defense Technology (2007)

  37. Wang, Z., Xu, X.: A survey on electronic image stabilization. J Image Graph 15(3), 470–480 (2010)

    Google Scholar 

  38. Chen, X.: Study on digital image stabilization technology for aerial opto-electric imaging system. Changchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences (2014)

  39. Han, T., Zhao, Y., Liu, S., et al.: Spatially constrained SURF feature point matching for UAV images. J. Image Graph. 18(6), 669–676 (2013)

    Google Scholar 

  40. He, L., Chao, Y., Suzuki, K., et al.: Fast connected-component labeling. Patt. Recogn. 42, 1977–1987 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

The project is supported by the National Natural Science Foundation of China (Grant No. 61503418), the Fundamental Research Funds for the Central Universities (South-Central University for Nationalities (Grant No. CZY16005)), Major projects of Guangdong Province (Grant No. 2015B090922007) and Foshan innovation team project (Grant No. 2015IT100143).

Author information

Authors and Affiliations

  1. School of Power and Mechanical Engineering, Wuhan University, Luojiashan Bayi Road No. 299, Wuchang District, Wuhan, 430072, Hubei, China

    Li Cheng & Gongping Wu

  2. College of Computer Science, South-Central University for Nationalities, Minzu Avenue, No. 182, Hongshan District, Wuhan, 430074, Hubei, China

    Li Cheng

Authors
  1. Li Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gongping Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gongping Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, L., Wu, G. Obstacles detection and depth estimation from monocular vision for inspection robot of high voltage transmission line. Cluster Comput 22 (Suppl 2), 2611–2627 (2019). https://doi.org/10.1007/s10586-017-1356-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1356-8

Keywords

Search

Navigation