Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.

[1]  Ronghua Liang,et al.  Hand-eye calibration with a new linear decomposition algorithm , 2008 .

[2]  Gerd Hirzinger,et al.  Active self-calibration of robotic eyes and hand-eye relationships with model identification , 1998, IEEE Trans. Robotics Autom..

[3]  Michel Dhome,et al.  Hand-eye calibration , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[4]  Hongdong Li,et al.  Rotation Averaging , 2013, International Journal of Computer Vision.

[5]  Ming Shao,et al.  Discriminative metric: Schatten norm vs. vector norm , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[6]  Frank Chongwoo Park,et al.  Robot sensor calibration: solving AX=XB on the Euclidean group , 1994, IEEE Trans. Robotics Autom..

[7]  I. Fassi,et al.  Hand to sensor calibration: A geometrical interpretation of the matrix equation AX = XB , 2005 .

[8]  Yiu Cheung Shiu,et al.  Calibration of wrist-mounted robotic sensors by solving homogeneous transform equations of the form AX=XB , 1989, IEEE Trans. Robotics Autom..

[9]  Jonathan Kelly,et al.  Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[10]  E. B. Andersen,et al.  Modern factor analysis , 1961 .

[11]  Richard I. Hartley,et al.  Global Optimization through Rotation Space Search , 2009, International Journal of Computer Vision.

[12]  Gerd Hirzinger,et al.  Optimal Hand-Eye Calibration , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Jong-Eun Ha.  Automatic detection of calibration markers on a chessboard , 2007 .

[14]  Jianhua Wang,et al.  An Approach to Improve Online Hand-Eye Calibration , 2005, IbPRIA.

[15]  Jun-Sik Kim,et al.  Structure-From-Motion in 3D Space Using 2D Lidars , 2017, Sensors.

[16]  Michal Havlena,et al.  Globally Optimal Hand-Eye Calibration Using Branch-and-Bound , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Roger Y. Tsai,et al.  A new technique for fully autonomous and efficient 3D robotics hand/eye calibration , 1988, IEEE Trans. Robotics Autom..

[18]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[20]  Zijian Zhao,et al.  Hand-eye calibration using convex optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[21]  Takeshi Oishi,et al.  LiDAR and Camera Calibration Using Motions Estimated by Sensor Fusion Odometry , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[22]  Yiu Cheung Shiu,et al.  A noise-tolerant algorithm for robotic hand-eye calibration with or without sensor orientation measurement , 1993, IEEE Trans. Syst. Man Cybern..

[23]  Mili Shah,et al.  An overview of robot-sensor calibration methods for evaluation of perception systems , 2012, PerMIS.

[24]  Ji-Yong Lee,et al.  Robot Head-Eye calibration using the Minimum Variance method , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[25]  Gregory S. Chirikjian,et al.  Simultaneous Hand-Eye and Robot-World Calibration by Solving the $AX=YB$ Problem Without Correspondence , 2016, IEEE Robotics and Automation Letters.

[26]  Jack C. K. Chou,et al.  Finding the Position and Orientation of a Sensor on a Robot Manipulator Using Quaternions , 1991, Int. J. Robotics Res..

[27]  Jan Heller,et al.  Hand-eye and robot-world calibration by global polynomial optimization , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).