Model-based planning of optimal sensor placements for inspection

We report a system for sensor planning, GASP, which is used to compute the optimal positions for inspection tasks using known imaging sensors and feature-based object models. GASP (general automatic sensor planning) uses a feature inspection representation (the FIR), which contains the explicit solution for the simplest sensor positioning problem. The FIR is generated off-line, and is exploited by GASP to compute on-line plans for more complex tasks, called inspection scripts. Viewpoint optimality is defined as a function of feature visibility and measurement reliability. Visibility is computed using an approximate model. Reliability of inspection depends on both the physical sensors acquiring the images and on the processing software; therefore we include both these components in a generalized sensor model. These predictions are based on experimental, quantitative assessment. We show how these are computed for a real generalized sensor, which includes a 3-D range imaging system, and software performing robust outlier removal, surface segmentation, object location and surface fitting. Finally, we demonstrate a complete inspection session involving 3-D object positioning, planning optimal position inspection, and feature measurement from the optimal viewpoint.

[1]  Jitendra Malik,et al.  Computing the Aspect Graph for Line Drawings of Polyhedral Objects , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Richard Durbin,et al.  An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.

[3]  Ruzena Bajcsy,et al.  Occlusions as a Guide for Planning the Next View , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Emanuele Trucco,et al.  Visibility scripts for active feature-based inspection , 1994, Pattern Recognit. Lett..

[5]  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..

[6]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[7]  E. Trucco,et al.  Computing surface-based representations from range images , 1992, Proceedings of the 1992 IEEE International Symposium on Intelligent Control.

[8]  Vaughan R. Pratt,et al.  Direct least-squares fitting of algebraic surfaces , 1987, SIGGRAPH.

[9]  Emanuele Trucco,et al.  Improving laser triangulation sensors using polarization , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Robert B. Fisher,et al.  Experiments in Curvature-Based Segmentation of Range Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Takeo Kanade,et al.  Sensor placement design for object pose determination with three light-stripe range finders , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[12]  Anil K. Jain,et al.  On reliable curvature estimation , 1989, CVPR.

[13]  Andrew M. Wallace,et al.  Model indexing and object recognition using 3D viewpoint invariance , 1997, Pattern Recognit..

[14]  Katsushi Ikeuchi,et al.  Why aspect graphs are not (yet) practical for computer vision , 1991, [1991 Proceedings] Workshop on Directions in Automated CAD-Based Vision.

[15]  Allen M. Waxman,et al.  Adaptive 3-D Object Recognition from Multiple Views , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[17]  Hiroshi Murase,et al.  Illumination planning for object recognition in structured environments , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Robert B. Fisher,et al.  Acquisition of consistent range data using local calibration , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[19]  A. Wallace,et al.  Outlier removal and discontinuity preserving smoothing of range data , 1996 .

[20]  Kevin W. Bowyer,et al.  Aspect graphs: An introduction and survey of recent results , 1990, Int. J. Imaging Syst. Technol..

[21]  Philippe Saint-Marc,et al.  A versatile PC-based range finding system , 1991, IEEE Trans. Robotics Autom..

[22]  Tomomasa Sato,et al.  Automatic planning of light source and camera placement for an active photometric stereo system , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[23]  R. Brockett.  Least squares matching problems , 1989 .

[24]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Konstantinos A. Tarabanis,et al.  A survey of sensor planning in computer vision , 1995, IEEE Trans. Robotics Autom..

[26]  Li-Dong Cai,et al.  A "Small Leakage" Model for Diffusion Smoothing of Image Data , 1989, IJCAI.

[27]  Masayoshi Kakikura,et al.  Occlusion avoidance of visual sensors based on a hand-eye action simulator system: HEAVEN , 1987, Adv. Robotics.

[28]  Charles R. Dyer,et al.  3-D multiview object representations for model-based object recognition , 1987, Pattern Recognit..

[29]  Roger Y. Tsai,et al.  Analytical characterization of the feature detectability constraints of resolution, focus, and field-of-view for vision sensor planning , 1994 .

[30]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[31]  Takeo Kanade,et al.  Modelling sensors: Toward automatic generation of object recognition program , 1989, Comput. Vis. Graph. Image Process..

[32]  Azriel Rosenfeld,et al.  3-D Shape Recovery Using Distributed Aspect Matching , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Guanghua Zhang,et al.  Registered depth and intensity data from an integrated vision sensor , 1994, Other Conferences.

[34]  Manickam Umasuthan.  Recognition and position estimation of 3D objects from range images using algebraic and movement invariants , 1995 .

[35]  K. Ikeuchi,et al.  Am Illumination Planner for Convex and Concave Lambertian Polyhedral Objects , 1995, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[36]  D. Eggert,et al.  Perspective projection aspect graphs of solids of revolution: an implementation , 1991, [1991 Proceedings] Workshop on Directions in Automated CAD-Based Vision.

[37]  Peter Kovesi,et al.  Automatic Sensor Placement from Vision Task Requirements , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Aviv Bergman,et al.  Determining the camera and light source location for a visual task , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[39]  Frank P. Ferrie,et al.  From Uncertainty to Visual Exploration , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  W. Eric L. Grimson,et al.  An implementation of a computational theory of visual surface interpolation , 1983, Comput. Vis. Graph. Image Process..

[41]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.