Laser ranging and video imaging for bin picking Faysal

This paper describes an imaging system that was developed to aid industrial bin picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The technology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface information that can be employed to guide the robotic arm. Texture was also provided by the video streams and applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and represents a step toward full automation.

[1]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[2]  Katsushi Ikeuchi,et al.  Generating an interpretation tree from a CAD model for 3D-object recognition in bin-picking tasks , 1987, International Journal of Computer Vision.

[3]  Akio Kosaka,et al.  Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[4]  Franc Solina,et al.  Segmentation and Recovery of Superquadrics , 2000, Computational Imaging and Vision.

[5]  Ruzena Bajcsy,et al.  Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[7]  Franc Solina,et al.  Superquadrics for Segmenting and Modeling Range Data , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[9]  Azriel Rosenfeld,et al.  From volumes to views: An approach to 3-D object recognition , 1992, CVGIP Image Underst..

[10]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Alan H. Barr,et al.  Global and local deformations of solid primitives , 1984, SIGGRAPH.

[12]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  W. Press,et al.  Numerical Recipes in Fortran: The Art of Scientific Computing.@@@Numerical Recipes in C: The Art of Scientific Computing. , 1994 .

[14]  Arun K. Sood,et al.  Range image segmentation combining edge-detection and region-growing techniques with applications sto robot bin-picking using vacuum gripper , 1990, IEEE Trans. Syst. Man Cybern..

[15]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[16]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[17]  Je L. EdwardsRobot Experimental State of the Art in 3d Object Recognition and Localization Using Range Data , 1995 .