3D Measurement of a Moving Object Using a Moving Camera Attached with a 6-Axis Sensor

Toshihiro Akamatsu; Fangyan Dong; Kaoru Hirota

doi:10.20965/jaciii.2014.p0736

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JACIII Vol.18 No.5 pp. 736-744

doi: 10.20965/jaciii.2014.p0736

(2014)

Paper:

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3D Measurement of a Moving Object Using a Moving Camera Attached with a 6-Axis Sensor

Toshihiro Akamatsu, Fangyan Dong, and Kaoru Hirota

Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:

July 5, 2013

Accepted:

April 18, 2014

Published:

September 20, 2014

Keywords:

3D measurement, 6-axis sensor, moving object, moving camera

Abstract

Measurement using a moving camera and a 6-axis sensor under the camera is proposed to determine the distance from the camera to the surface of a moving object and the object’s position movement in two continuous frame images. This makes it possible to measure the 3D position of a moving object at half of the computational cost while keeping the same accuracy as using a stereo camera. 3D measurement experiments with several original images show that the computational time using the proposal is about twice as fast as that of a stereo camera. The proposed method is planning to be used to vehicles or mobile robots avoid obstacles, and its use as a depth meter is also investigated.

Cite this article as:

T. Akamatsu, F. Dong, and K. Hirota, “3D Measurement of a Moving Object Using a Moving Camera Attached with a 6-Axis Sensor,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.5, pp. 736-744, 2014.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] Z. Hu and Z. Tan, “Depth recovery and affine reconstruction under camera pure translation,” Pattern Recognition, Vol.40, Iss.10, pp. 2826-2836, 2007.

[2] [2] S. Fuchs and G. Hirzinger, “Extrinsic and Depth Calibration of ToFcameras,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-6, 2008.

[3] [3] R. Hartlry and C. Silpa-Anan, “Reconstruction from two views using approximate calibration,” Proc. 5th Asian Conf. Comput. Vision, Vol.1, pp. 338-343, 2002.

[4] [4] K. Yamada, Y. Kanazawa, K. Kanatani, and Y. Sugaya, “Latest Algorithm of 3D-Reconstruction from Two Views,” 2009-CVIM-168-15, pp. 1-8, 2009.

[5] [5] K. Kanatani and H. Mishima, “3-D Reconstruction from Two Uncalibrated Views and Its Reliability Evaluation,” 2000-CVIM-121-4, pp. 25-32, 2000.

[6] [6] Z. Zhang, R. Deriche, O. D. Faugeras, and Q. Luong, “A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry,” Research Report 2273, 1994.

[7] [7] O. D. Faugeras, “Stratification of three-dimensional vision: projective, affine, and metric representations,” J. of the Optical Society of America, Vol.12, No.3, pp. 465-484, 1995.

[8] [8] R. Hartley, “In Defense of the Eight-Point Algorithm,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.6, pp. 580-593, 1997.

[9] [9] T.Mukai and N. Ohnishi, “The Recovery of Object Shape and Camera Motion Using a Sensing System with a Video Camera and a Gyro Sensor,” Proc. of the 7th IEEE Int. Conf. on Computer Vision, Vol.I, pp. 411-417, 1999.

[10] [10] R. Hartley and A. Zisserman, “Multiple View Geometry in Computer Vision Second Edition,” Cambridge University Press, March 2004.

[11] [11] T. Kagawa and Y. Uno, “Gait Pattern Generation for a Power-Assist Device of Paraplegic Gait,” Proc. of the 18th IEEE Int. Symp. on Robot and Human Interactive Communication, pp. 633-638, 2009.

[12] [12] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF,” Proc. of the IEEE Int. Conf. on Computer Vision, pp.2564-2571, 2011.

[13] [13] M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. of the ACM 24, pp. 381-395, 1981.