Abstract
Since the stereo vision has the advantages of providing autonomous navigation information for unmanned aerial vehicle (UAV) without environmental constraints. In this paper, the UAV is combined with stereo vision to perform the power line inspection. With the stereo vision system the line segments are reconstructed to obtain the orientation information, which provides reference for the attitude adjustment of the UAV. During the line construction, in order to make the line inspection more natural and reasonable, an efficient stereo line matching method which combines feature matching and block matching is proposed with artificial screening. Firstly, the image returned by the on-board binocular camera is used to click on the specific line to eliminate interference in the clutter background. The region of interest is filtered in the Hough space of the left image, and the Iterative Closest Point algorithm is used to filter the matching area in the right image. Secondly, The SAD matching cost function is used to merely recover the disparity map of the region of interest for the stereo frames after line screening. The reconstruction of a particular line is performed by least squares. To the performance of the proposed approach, the cyclic experiments on the ground are carried out. Based on the results, the proposed method improves the angle measurement accuracy of line segments. The orientation information can be utilized to provide reference for the attitude adjustment of the UAV.
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Woodley, B., Jones II, H., LeMaster, E., Frew, E.: Carrier phase GPS and computer vision for control of an autonomous helicopter. ION GPS-96, Kansas City, Missouri, September 1996
Gomez-Ojeda, R., Gonzalez-Jimenez, J.: Robust stereo visual odometry through a probabilistic combination of points and line segments. In: IEEE International Conference on Robotics & Automation. IEEE (2016)
A stereo vision system for UAV guidance. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2009)
Stefanik, K.V., Gassaway, J.C., Kochersberger, K., et al.: UAV-Based stereo vision for rapid aerial terrain mapping. GIScience & Remote Sens. 48(1), 24–49 (2011)
Campoy, P., Garcia, P.J., Barrientos, A., et al.: An Stereoscopic Vision System Guiding an Autonomous Helicopter for Overhead Power Cable Inspection (2001)
Marr, D., Poggio, T.: Cooperative computation of stereo disparity. Science 194, 283–287 (1976)
Ambrosch, K., Kubinger, W., Humenberger, M., et al.: Hardware implementation of an SAD based stereo vision algorithm. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2007)
Banks, J., Bennamoun, M., Corke, P.: Non-parametric techniques for fast and robust stereo matching. In: Proceedings of IEEE Conference on Speech and Image Technologies for Computing and Telecommunications (1997)
Koletschka, T., Puig, L., Daniilidis, K.: MEVO: multi-environment stereo visual odometry. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 14–18 September 2014
Witt, J., Weltin, U.: Robust stereo visual odometry using iterative closest multiple lines. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2013
Besl, P.J., Mckay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (2002)
Acknowledgments
This project was supported by Shaanxi Science and Technology Co-ordinated Innovation Project (Grant Nos. 2016KTZDGY-02-03).
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Wang, J., Zhang, D., Sun, X., Zhang, X. (2019). A Stereo Matching Method Combining Feature and Area Information for Power Line Inspection. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_41
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DOI: https://doi.org/10.1007/978-3-030-27541-9_41
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