The Research of Binocular Ranging System on Independent Mobile Robot

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Timeliness and accuracy is a key to be resolved on robot binocular measurement. In this paper, a kind of robot vision projection has been completely established. It analyzes the principle of binocular ranging in three aspects, makes the calculation concise and easy to understand, and expands the range of effective distance. On binocular image processing, we have proposed a gray-scale computing for, firstly, generating characteristic area, then, executing template matching in the area, finally, extracting feature points and matching them in the templates. It ensures certain robustness to noise spots and tries its best to avoid mismatches. The experiments show that the robot vision system has a better accuracy and a low time complexity, and the robot can react in real time.

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603-608

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February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Zhu Kun, Yang Tangwen, Ruan Qiuqi, et al: Real-Time Tracking and Measuring of Moving Objects Based on Binocular Vision [J]. Robot, Vol. 31 (2009) No. 4, p.327.

Google Scholar

[2] Ma S. D: A Self-calibration technique for active vision systems[J]. IEEE Transactions on Robotics and Automation, Vol. 12(1996)No. 1, p.114.

DOI: 10.1109/70.481755

Google Scholar

[3] Zhang Z Y: A flexible new technique camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22(2000)No. 11, p, 1330.

DOI: 10.1109/34.888718

Google Scholar

[4] Chang Danhua, ChenChao, Cheng Defang, et al: Simplified Binocular Stereo Distance-Measurement System Based on Embedded Platform[J]. Electronic Measurement Technology, Vol. 31(2008)No. 8, p.61.

Google Scholar

[5] Daniel Scharstein, Richard Szeliski: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms [J]. IEEE International Journal of Computer Vision, Vol. 47(2002)No. 1, p.7.

DOI: 10.1109/smbv.2001.988771

Google Scholar

[6] Okutomi M, Kanade T: A Multiple-Baseline Stereo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15(1993) No. 4, p.353.

DOI: 10.1109/34.206955

Google Scholar

[7] M. Li, Z.Z. Wang, J.Y. Wang, et al: The Application of Binocular Ranging in the Traffic Accident Processing[C]/8th International Conference on Machine Learning and Cybernetics (Baoding, IEEE, 2265-2269, 2009).

DOI: 10.1109/icmlc.2009.5212234

Google Scholar

[8] Zhang Lingtao, Qu Daokui, Xu Fang: An Improved Stereo Matching Algorithm Based on Graph Cuts [J]. Robot, Vol. 32(2010) No. 1, p.104.

DOI: 10.3724/sp.j.1218.2010.00104

Google Scholar

[9] Fang Hui, Yang Ming, Yang Ruqing: Ground Feature Points Matching Based Global Localization for Driverless Vehicles [J]. Robot, Vol. 32(2010) No. 1, p.55.

DOI: 10.3724/sp.j.1218.2010.00055

Google Scholar

[10] Li Qiang, Zhang Bo: A fastmatching algorithm based on image gray value[J]. Journal of Software, Vol. 17 (2006) No. 2, p.216.

Google Scholar

[11] Bradski G, Kaehler A: Learning OpenCV (Photocopy Edition)[M](Southeast University Press, NanJing 2009).

Google Scholar