Abstract
The MirosSot and the AndroSot soccer robots have the ability to recognize, and navigate within, their environments without human intervention. An overhead global camera, usually at a fixed position, is used for the robot’s vision. Because of the lens distortion, images obtained from the camera do not accurately represent the robot’s environment. The distortions affect the coordinates. A technique to calibrate the camera is required to transform the skewed coordinates of the objects in the image to the physical coordinates, which define their real-world position. In this study, a method is proposed for camera calibration using an artificial neural network (ANN) in a two-step process. First, ANN was used to select the camera height and the lens focal lengths for high accuracy. Second, ANN was used to map a coordinate transformation from the camera coordinates to the physical coordinates. During the learning process, the weight of each node in the ANN model changed until the best architecture is reached. The experiments thus resulted in an optimum ANN architecture of 2×4×25×2. The accuracy and efficiency of the camera calibration method were obtained by relearning using the ANN whenever changes to the environmental occurred. Relearning was done using the new input data set for each respective environmental change. Based on our experiments, the average transformation error of the calibration method, using many types of camera, camera positions, camera heights, lens sizes, and focal lengths, was 0.18283 cm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Relf, C.G.: Image Acquisition and Processing with LabVIEW. CRC Press (2004)
Fraga, L.G., Schultze, O.: Direct Calibration by Fitting of Cuboids to a Single Image Using Diferential Evolution. International Journals of Computer Vision, 119–127 (2008)
Chen, L., Zheng, X., Hong, J., Qiao, Y., Wang, Y.: A Novel Method for Adjusting CCD Camera in Geometrical Calibration Based on a Two-dimensional Turntable. International Journal of Optic for Light and Electron Optics 121(5), 8–11 (2008)
Mendonca, M., Silva, I.N., Castanho, J.E.C.: Camera Calibration Using Neural Network. In: Proc of Int Conf in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 61–64 (2002)
Peng, E., Li, L.: Camera Calibration Using One-Dimensional Information and Its Applications in Both Controlled and Uncontrolled Environments. International Journal on Pattern Recognition 43(3), 1188–1198 (2010)
Claus, D., Fritzgibbon, A.W.: A Rational Lens Distortion Model for General Cameras. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 213–219. IEEE Computer Society (2005)
Sugawa, R., Takatsuji, M., Echigo, T., Yagi, Y.: Calibration of Lens Distortion by Structured-Light Scanning. In: Proceedings of International Conference on Intelligent Robots and Systems, pp. 832–837 (2005)
Hartley, R., Kang, S.B.: Parameter-Free Radial Distortion Correction with Center of Distortion Estimation. International Journal of IEEE Transaction on Pattern Analysis and Machine Intelligence 29(8), 1309–1321 (2007)
Lee, D.J., Cha, S.S., Park, J.H.: Stereoscopic Vision Calibration for Three-dimensional Tracking Velocimetry Based on Artificial Neural Networks. In: Proceedings of SPIE International Symposium, Optical Science and Technology, pp. 1–13 (2003)
Kim, J.K., Kweon, I.S.: Camera Calibration Based in Arbitrary Parallelograms. International Journal of Computer Vision and Image Understanding, 1–10 (2009)
Miks, A., Novak, J.: Estimation of Accuracy of Optical Measuring Systems with Respect to Object Distance. International Journal of Optics Express 19(15), 14300–14314 (2011)
Hartley, R., Kang, S.B.: Parameter-Free Radial Distortion Correction with Center of Distortion Estimation. International Journal of IEEE Transaction on Pattern Analysis and Machine Intelligence 29(8), 1309–1321 (2007)
Kusumadewi, S.: Artificial Intellegence: Teori dan Aplikasinya. Penerbit Graha Ilmu, Yogyakarta (2003)
Haykin, S.: Neural Network: A Comprehensive Foundation. New York MacMilan, Penerbit (1994)
Xiaobo, C., Haifeng, G., Yinghua, Y., Shukai, Q.: A New Method for Coplanar Camera Calibration Based on Neural Network. In: Proc. of Int. Conf. on Computer Application and System Modeling, pp. 617–621 (2010)
Woo, D.-M., Park, D.-C.: Implicit camera calibration using an artificial neural network. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4233, pp. 641–650. Springer, Heidelberg (2006)
Woo, D.M., Park, D.C.: Implicit Camera Calibration using Multilayer Perceptron Type Neural network. In: Proceedings of the First Asian Conference on Intelligent Information and Database System, pp. 313–317 (2009a)
Woo, D.-M., Park, D.-C.: Implicit camera calibration based on a nonlinear modeling function of an artificial neural network. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009, Part I. LNCS, vol. 5551, pp. 967–975. Springer, Heidelberg (2009)
Wang, H., Cao, G., Xu, H., Wang, P.: Application of Neural Network on Distortion Correction Based of Standard Grid. In: Proceedings of International Conference on Mechatronics and Application, pp. 2717–2722 (2009)
Lee, J.H., Oh, S.Y.: An Absolute Robot Pose Estimation System Based on a Ceiling Camera Image Using a Neural Network. In: Proceedings of International Conference on System, Man, and Cybernetics, pp. 3839–3843 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pratomo, A.H., Zakaria, M.S., Nasrudin, M.F., Prabuwono, A.S., Liong, CY., Azmi, I. (2015). Robust Camera Calibration for the MiroSot and the AndroSot Vision Systems Using Artificial Neural Networks. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-16841-8_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
eBook Packages: EngineeringEngineering (R0)