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Development of Borehole Imaging Method with Using Visual-SLAM

  • Tsuneo KagawaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

Borehole imaging is a method to investigate inside vertical bored hole with an optical camera. It is very important for construction and civil engineering that can accurately investigate the inner ground and geology at low cost. Recently borehole imaging method has also become important in terms of natural disaster prevention, such as landslides. However, easily surveyed cameras are difficult to control their position or pose in the hole because they are always rolling and rotating in the ground. In this study, our purpose is to realize a high-precision survey using cameras at low cost. We consider the introduction of visual SLAM (Simultaneous Localization and Mapping), which is used for autonomous robot navigation, self-driving car or smart phone augmented reality application. In this paper we discuss about development of borehole imaging method with using visual-SLAM.

References

  1. 1.
    Chite, P.P., Rana, J.G., Bhambare, R.R., More, V.A., Kadu, R.A., Bendre, M.R.: IRIS recognition system using ICA, PCA, Daugman’s rubber sheet model together. Int. J. Comput. Technol. Electron. Eng. 2(1), 16–23 (2012)Google Scholar
  2. 2.
    Wang, S., Luo, S., Huang, Y., Zheng, J.Y., Dai, P., Han, Q.: Railroad online: acquiring and visualizing route panoramas. Vis. Comput. 30(9), 1045–1057 (2014)CrossRefGoogle Scholar
  3. 3.
    Rousso, B., Peleg, S., Finci, I., Rav-Acha, A.: Universal mosaicing using pipe projection. In: Proceedings of Sixth International Conference on Computer Vision, pp. 945–950 (1998)Google Scholar
  4. 4.
    Cao, M., Deng, Z., Rai, L., Teng, S., Zhao, M., Collier, M.: Generating panoramic unfolded image from borehole video acquired through APBT. Multimedia Tools Appl. 77(19), 25149–25179 (2018)CrossRefGoogle Scholar
  5. 5.
    Sebastian, T., Wolfram, B., Dieter, F., Ryuichi, U.: Probablistic Robotics (in Japanese), MyNavi Shuppan, ISBN 978-4839952983, (2016)Google Scholar
  6. 6.
    Chuanying, W., Xianjian, Z., Zengqiang, H., Jinchao, W., Yiteng, W.: The automatic interpretation of structural plane parameters in borehole camera images from drilling engineering. J. Pet. Sci. Eng. 154, 417–424 (2017)CrossRefGoogle Scholar
  7. 7.
    Xi-Ning, L., Jin-Song, S., Wu-Yang, Y., Zhen-Ling, L.: Automatic fracture-vug identification and extraction from electric imaging logging data based on path morphology. Pet. Sci. 16, 58–76 (2018)Google Scholar
  8. 8.
    Rommed, A.Q.C., Diego, C.C., Renato, M.S., Evandro, J.R.P., Fabiana, R.L., Esteban, W.G.C.: Improving accuracy of automatic fracture detection in borehole images with deep learning and GPUs. In: 30th International Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 345–350 (2017)Google Scholar
  9. 9.
    Mariusz, M., Magdalena, H., Norbert, S.: The application of the automatic search for visually similar geological layers in a borehole in introscopic camera recordings. Measurement 85, 142–151 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Oita UniversityOita CityJapan

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