A real-time and robust feature detection method using hierarchical strategy and modified Kalman filter for thick plate seam tracking

被引:5
作者
Kiddee P. [1 ]
Fang Z. [1 ]
Tan M. [1 ]
机构
[1] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
来源
Kiddee, Prasarn (prasarnkid@gmail.com) | 1600年 / Inderscience Publishers, 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 11期
基金
中国国家自然科学基金;
关键词
Coarse-to-fine strategy; Feature detection; Modified Kalman filter; Real time; Robustness; Seam tracking;
D O I
10.1504/IJAAC.2017.087054
中图分类号
学科分类号
摘要
Feature detection is an essential and important part in weld seam tracking of automated welding robots. In thick plate seam tracking, a profilometer based on structured light is commonly employed. Features from light stripe of the structured light will be extracted and used as primary information in visual servoing control. The accuracy, robustness, and computational cost are the main aspects of the feature detection. They directly affect the quality of the tracking. In this paper, cross mark created by cross line structured light (CLSL) is taken into account, and handled as the feature. It can be used as a pinpoint for seam tracking. Firstly, the cross mark is hierarchically estimated by coarse-to-fine strategy. In coarse estimation step, the random sample consensus (RANSAC) algorithm is applied to compute the feature position. Subsequently, the mean shift algorithm is used to estimate the precise feature position in the fine estimation step. Finally, the robustness of the detection is improved by the modified Kalman filter algorithm. The experimental results verify that the feature position estimated by the proposed method is robust. Moreover, the coarse-to-fine strategy can reduce a huge computational cost in the detection. And therefore, the detection method is proper for being used in real-time thick plate seam tracking. Copyright © 2017 Inderscience Enterprises Ltd.
引用
收藏
页码:428 / 446
页数:18
相关论文
共 21 条
  • [1] Chabir K., Sauter D., Abdelkrim M.N., Gayed M.K.B., Robust fault diagnosis of networked control systems via Kalman filtering, International Journal of Automation and Control, 4, 3, pp. 343-356, (2010)
  • [2] Comaniciu D., Meer P., Mean shift: A robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 5, pp. 603-619, (2002)
  • [3] Fang Z.J., Xu D., Tan M., Visual seam tracking system for butt weld of thin plate, International Journal of Advance Manufacturing Technology, Vo, 49, 5, pp. 519-526, (2010)
  • [4] Fischler M.A., Bolles R.C., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, IT Communications of the ACM, 4, 6, pp. 381-395, (1981)
  • [5] Gan Z.X., Tang Q., Visual Sensing and Its Applications: Integration of Laser Sensors to Industrial Robots, pp. 3-4, (2011)
  • [6] Gonzalez R.C., Woods R.E., Digital Image Processing, (2002)
  • [7] Huber P.J., Onchetti E.M., Robust Statistics, (2009)
  • [8] Hutchinson S., Hager G.D., Corke P., A tutorial on visual servo control, IEEE Transaction on Robotics and Automation, 12, 5, pp. 651-670, (1996)
  • [9] Kiddee P., Fang Z.J., Tan M., A simple technique for structured light calibration in welding robots, IEEE International Conference on Robotics and Biomimetics, pp. 596-601, (2015)
  • [10] Kim S.W., Ha M.M., Yang Y.M., State-of-charge estimation of electric vehicles using Kalman filter for a harsh environment