Real-time 3D model-based tracking of work-piece with monocular camera

The tracking of 3D work pieces with real-time speed is becoming more and more important for some industrial tasks, such as work pieces grasping and assembly, especially in complex environment. In this paper, we propose a novel real-time 3D model-based tracking method for work pieces with monocular camera, which can provide accurate 3D location information of the tracking object continuously. Three processes are designed in the proposed method, i.e., the offline global model library generation process, the online dynamic library updating and selection process, and the 3D work piece localization process. The method is suitable for the texture-less work-pieces in industrial applications. In the offline global model library generation process, the CAD models of the work piece are used to generate a set of discrete 2D views matching libraries. In the online dynamic library updating and selection process, the previous 3D location information of the work piece is used to predict the following location range, and a dynamic discrete views library with a small number of models is selected for localization. Then, the work piece is localized with high-precision and real time speed in the 3D work piece localization process. The small range of the library enables a real-time matching. Experimental results demonstrate the high accuracy and high efficiency of the proposed method.

[1]  Ivan Poupyrev,et al.  Virtual object manipulation on a table-top AR environment , 2000, Proceedings IEEE and ACM International Symposium on Augmented Reality (ISAR 2000).

[2]  Marcus A. Magnor,et al.  A graphics hardware implementation of the generalized Hough transform for fast object recognition, scale, and 3D pose detection , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[3]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[4]  Linda G. Shapiro,et al.  3D Object Recognition and Pose with Relational Indexing , 2000, Comput. Vis. Image Underst..

[5]  Khoi Nguyen,et al.  Computer-vision-based registration techniques for augmented reality , 1996, Other Conferences.

[6]  Lucas Paletta,et al.  Appearance-based active object recognition , 2000, Image Vis. Comput..

[7]  Mark A. Livingston,et al.  Superior augmented reality registration by integrating landmark tracking and magnetic tracking , 1996, SIGGRAPH.

[8]  Philip David,et al.  Simultaneous pose and correspondence determination using line features , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[9]  Vincent Lepetit,et al.  Combining edge and texture information for real-time accurate 3D camera tracking , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[10]  W. Eric L. Grimson,et al.  On the Verification of Hypothesized Matches in Model-Based Recognition , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Philip David,et al.  Object recognition in high clutter images using line features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[12]  Éric Marchand,et al.  A real-time tracker for markerless augmented reality , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[13]  Patrick Bouthemy,et al.  A 2D-3D model-based approach to real-time visual tracking , 2001, Image Vis. Comput..

[14]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  J. H. M. Byne,et al.  A CAD-based computer vision system , 1998, Image Vis. Comput..

[16]  Markus Ulrich,et al.  Combining Scale-Space and Similarity-Based Aspect Graphs for Fast 3D Object Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.