|
|
An Optimal Projection Plane-Based Spatial Circle Measurement Method Using Stereo Vision System |
LI Zhengyuan1, MA Xin1, LI Yibin1 |
1. Center for Robotics, School of Control Science and Engineering, Shandong University, Ji'nan 250061 |
|
|
Abstract The accuracy of the existing measurement method based on binocular stereo vision depends on the accuracy of calibration, and the accuracy of measurement decreases when the spatial circle is occluded. Firstly, the reconstruction accuracy of point affected by the stereo matching error of points on projection curves is analyzed in the presence of external parameter error of the binocular stereo vision system. Then, based on the conclusion of error analysis, a new method for measuring the position and orientation of spatial circle is designed. Using the edge points selecting algorithm, the points of projection curves are selected. The circle is then reconstructed using the points with small stereo matching error. The projection of the reconstructive points on the optimal projection plane based on nonlinear optimization in the direction depth is utilized for fitting spatial circle to obtain the position and orientation. Experimental results prove the effectiveness of the proposed algorithm.
|
Received: 15 December 2018
|
|
Fund:Supported by the Joint Fund of National Natural Science Foundation of China and Shandong Province(No. U1706228), National Natural Science Foundation of China(No.61673245), Key Research Plan of Shandong Province (No.2016ZDJS02A07) |
Corresponding Authors:
MA Xin, Ph.D., professor. Her research interests include artificial intelligence, machine vision and mobile robots.
|
About author:: LI Zhengyuan, master student. His research interests include visual perception.LI Yibin, Ph.D., professor. His research interests include robotics and intelligent control theories. |
|
|
|
[1] LUHMANN T. Close Range Photogrammetry for Industrial Applications. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(6): 558-569. [2] DENIZ C, CAKIR M. In-Line Stereo-Camera Assisted Robotic Spot Welding Quality Control System. Industrial Robot: An International Journal, 2018, 45(1): 54-63. [3] LEE Y, LEE S. Object Recognition and Pose Estimation Based on 3D Planar Closed Loop Boundaries // Proc of the IEEE International Conference on Automation Science and Engineering. Washington, USA: IEEE, 2016: 245-250. [4] EBERLI D, SCARAMUZZA D, WEISS S, et al. Vision Based Position Control for MAVs Using One Single Circular Landmark. Journal of Intelligent and Robotic Systems, 2011, 61(1/2/3/4): 495-512. [5] PERKINS W A. INSPECTOR: A Computer Vision System that Learns to Inspect Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5(6): 584-592. [6] WU B, XUE T, YE S H. A Two-Step Method for Spatial Circle Orientation with a Structured Light Vision Sensor and Error Analysis. Measurement Science and Technology, 2010, 21(7). DOI: 10.1088/0957-0233/21/7/075105. [7] GAO Y, SHAO S Y, FENG Q B. A New Method for Dynamically Measuring Diameters of Train Wheels Using Line Structured Light Visual Sensor // Proc of the Symposium on Photonics and Optoelectronics. Washington, USA: IEEE, 2012. DOI: 10.1109/SOPO.2012.6270922. [8] GONG Z, SUN J H, ZHANG G J. Dynamic Structured-Light Measurement for Wheel Diameter Based on the Cycloid Constraint. Applied Optics, 2016, 55(1): 198-207. [9] MA X, FENG J B, LI Y B, et al. Active 6-D Position-Pose Estimation of a Spatial Circle Using Monocular Eye-in-Hand System. International Journal of Advanced Robotic Systems, 2018, 15(1). DOI: 10.1177/1729881417753692. [10] HU Y Q, MA Y, XU M, et al. Measurement Algorithm of Mounting Holes Based on Binocular Stereo Vision // Proc of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems. Washington, USA: IEEE, 2016: 489-492. [11] SAFAEE-RAD R, TCHOUKANOV I, SMITH K C, et al. Three-Dimensional Location Estimation of Circular Features for Machine Vision. IEEE Transactions on Robotics and Automation, 1992, 8(5): 624-640. [12] QUAN L. Conic Reconstruction and Correspondence from Two Views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(2): 151-160. [13] XU W F, XUE Q, LIU H D, et al. A Pose Measurement Method of a Non-cooperative GEO Spacecraft Based on Stereo Vision // Proc of the 12th International Conference on Control Automation Robotics and Vision. Washington, USA: IEEE, 2012: 966-971. [14] LOWE D G. Fitting Parameterized Three-Dimensional Models to Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(5): 441-450. [15] MALASSIOTIS S, STRINTZIS M G. Stereo Vision System for Precision Dimensional Inspection of 3D Holes. Machine Vision and Applications, 2003, 15(2): 101-113. [16] 周富强,张广军,江 洁.空间圆几何参数的非接触高精度测量方法.仪器仪表学报, 2004, 25(5): 604-607. (ZHOU F Q, ZHANG G J, JIANG J. High Accurate Non-contact Method for Measuring Geometric Parameters of Spatial Circle. Chinese Journal of Scientific Instrument, 2004, 25(5): 604-607.) [17] ZHOU F Q, WANG Y X, PENG B, et al. A Novel Way of Understanding for Calibrating Stereo Vision Sensor Constructed by a Single Camera and Mirrors. Measurement, 2013, 46(3): 1147-1160. [18] CHEN X, LIN G Y. Wheel Center Detection Based on Stereo Vi-sion. Journal of Southeast University(English Edition), 2013, 29(2): 175-181. [19] ZHANG Z Y. Determining the Epipolar Geometry and Its Uncertainty: A Review. International Journal of Computer Vision, 1998, 27(2): 161-195. [20] SCHARSTEIN D, SZELISKI R. A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision, 2002, 47(1/2/3): 7-42. [21] STEGER C, ULRICH M, WIEDEMANN C. Machine Vision Algorithms and Applications. New York, USA: Wiley, 2008. [22] CANNY J. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698. [23] HARTLEY R I, STURM P. Triangulation. Computer Vision and Image Understanding, 1997, 68(2): 146-157. |
|
|
|