Object Recognition and Localization from Scanning Beam Sensors

Model based object recognition and object localization are fundamental problems in industrial automation. The authors present techniques which use a scanner composed of binary light beam sensors to quickly recognize objects (as fast as 5 microseconds) and to accurately localize objects (0.025 millimeters), and they describe localization and recognition experiments and present results. Binary sensors only sense whether the part is present or absent at a particular location, and their high performance is due to their simple specification. Fast recognition is achieved by using indexing to solve the correspondence problem, the problem of interpreting the sensed data as model features; indexing involves using the sensed data to directly look up the correspondence information using a precomputed indexing table. Since each experiment only produces a single indexing vector, indexing tables need to be complete; in this paper the authors detail a complete indexing construction method for flat polygonal and polyhedral objects.

[1]  Edith Schonberg,et al.  Two-Dimensional, Model-Based, Boundary Matching Using Footprints , 1986 .

[2]  Micha Sharir,et al.  Identification of Partially Obscured Objects in Two and Three Dimensions by Matching Noisy Characteristic Curves , 1987 .

[3]  Aaron S. Wallack,et al.  Complete indexing strategies for sparse sensing techniques , 1995, Proceedings. IEEE International Symposium on Assembly and Task Planning.

[4]  John F. Canny,et al.  A RISC approach to robotics , 1994, IEEE Robotics & Automation Magazine.

[5]  W. Eric L. Grimson Sensing Strategies for Disambiguating Among Multiple Objects in Known Poses , 1990, Autonomous Robot Vehicles.

[6]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

[7]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  David W. Jacobs,et al.  Space and Time Bounds on Indexing 3D Models from 2D Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..