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Model-based view planning

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Abstract

This paper presents a multi-phase, model-based approach to view planning for automated, high fidelity object inspection or reconstruction by means of laser scanning range sensors. We describe the critical phase, fine modeling, in detail. Quality objectives and performance measures are defined. Camera and positioning system performance is modeled statistically. A theoretical framework is presented. The method is applicable to a broad class of objects with reasonable geometry and reflectance properties. Sampling of object surface and viewpoint space is characterized, including measurement and pose errors. The technique is generalizable for common range cameras and positioning systems.

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References

  1. Banta, J., Abidi, M.: Autonomous placement of a range sensor for acquisition of optimal 3D models. In: Proc. IEEE 22nd Int. Conf. on Industrial Electronics, Control and Instrumentation, Taipei, pp. 1583–1588 (1996)

  2. Batchelor, B.: A prolog lighting advisor. In: Proc. SPIE Intell. Robots and Computer Vision VIII: Systems and Applications, vol. 1193, pp. 295–302 (1989)

  3. Beasley J. and Chu P. (1996). A genetic algorithm for the set covering problem. Eur. J. Oper. Res. 94: 392–404

    Article  MATH  Google Scholar 

  4. Beraldin, J.A., El-Hakim, S., Cournoyer, L.: Practical range camera calibration. In: Proc. Videometrics II, SPIE, Boston, vol. 2067, pp. 21–30 (1993)

  5. Besl P. (1989). Range image sensors. In: Sanz, J. (eds) Advances in Machine Vision, pp. Springer, New York

    Google Scholar 

  6. Besl P. and McKay N. (1992). A method for registration of 3D shapes. IEEE Trans. PAMI 14(2): 239–256

    Google Scholar 

  7. Blais F. (2004). Review of 20 years of range sensor development. J. Electron. Imaging 13(1): 231–243

    Article  Google Scholar 

  8. Blais, F., Rioux, M., Beraldin, J.A.: Practical considerations for a design of a high precision 3D laser scanning system. In: Proc. SPIE Conf. Optomechanical and Electro-Optical Design of Industrial Systems, Dearborn, vol. 959, pp. 225–246 (1988)

  9. Caprara A., Fischetti M. and Toth P. (2000). Algorithms for the set covering problem. Ann. Oper. Res. 98: 353–371

    Article  MATH  MathSciNet  Google Scholar 

  10. Cauchick-Miguel P., King T. and Davis J. (1996). CMM verification: a survey. Measurement 17(1): 1–16

    Article  Google Scholar 

  11. Ceria S., Nobili P. and Sassano A. (1998). A lagrangian-based heuristic for large-scale set covering problems. Math. Program. 81: 215–228

    MathSciNet  Google Scholar 

  12. Chen S. and Li Y. (2004). Automatic sensor placement for model-based robot vision. IEEE Trans. Systems Man Cybern. Part B: Cybern. 34(1): 393–408

    Article  Google Scholar 

  13. Christofides, N.: Worst-case analysis of a new heuristic for the travelling salesman problem. Tech. Rep. 388, Grad. School of Ind. Admin., Carnegie Mellon University (1976)

  14. Connolly, C.: The determination of next best views. In: Proc. IEEE Int. Conf. on Robotics and Automation, pp. 432–435 (1985)

  15. Cowan C. and Kovesi P. (1988). Automatic sensor placement from vision task requirements. IEEE Trans. PAMI 10(3): 407–416

    Google Scholar 

  16. Curless, B., Levoy, M.: Better optical triangulation through space-time analysis. In: SIGGRAPH ’96, pp. 1–10 (1996)

  17. El-Hakim, S., Beraldin, J.A.: Configuration design for sensor integration. In: Proc. Videometrics IV, SPIE, Philadelphia, vol. 2598, pp. 274–285 (1995)

  18. Faugeras O., Mundy J., Ahuja N., Dyer C., Pentland A., Jain R. and Ikeuchi K. (1992). Why aspect graphs are not (yet) practical for computer vision. CVGIP: Image Underst. 55(2): 212–218

    Article  MATH  Google Scholar 

  19. Fisher M. and Wolsey L. (1982). On the greedy heuristic for covering and packing problems. SIAM J. Algebr. Discret. Methods 3(4): 584–591

    Article  MATH  MathSciNet  Google Scholar 

  20. Garcia, M., Velazquez, S., Sappa, A.: A two-stage algorithm for planning the next view from range images. In: British Machine Vision Conference 1998, pp. 720–729 (1998)

  21. Grossman T. and Wool A. (1997). Computational experience with approximation algorithms for the set covering problem. Eur. J. Oper. Res. 101: 81–92

    Article  MATH  Google Scholar 

  22. Kitamura, Y., Sato, H., Tamura, H.: An expert system for industrial machine vision. In: Proc. 10th Int. Conf. on Pattern Recognition, pp. 771–773 (1990)

  23. Lamb, D., Baird, D., Greenspan, M.: An automation system for industrial 3D laser digitizing. In: 2nd Int. Conf. on 3D Digital Imaging and Modeling, Ottawa, pp. 148–157 (1999)

  24. Lawler E., Lenstra J., Rinnooy Kan A. and Shmoys D. (1985). The Traveling Salesman Problem: a Guided Tour of Combinatorial Optimization. Wiley, New York

    MATH  Google Scholar 

  25. Machida, T., Takemura, H., Yokoya, N.: Dense estimation of surface reflectance parameters by selecting optimum illumination conditions. In: 14th Int. Conf. on Vision Interface, Ottawa, pp. 244–251 (2001)

  26. Massios, N., Fisher, R.: A best next view selection algorithm incorporating a quality criterion. In: British Machine Vision Conference 1998, pp. 780–789 (September, 1998)

  27. Maver J. and Bajcsy R. (1993). Occlusions as a guide for planning the next view. IEEE Trans. PAMI 17(5): 417–433

    Google Scholar 

  28. Newman T. and Jain A. (1995). A survey of automated visual inspection. Comput. Vis. Image Underst. 61(2): 231–262

    Article  Google Scholar 

  29. Novini, A.: Lighting and optics expert system for machine vision. In: Proc. SPIE Optics, Illumination, and Image Sensing for Machine Vision III, vol. 1005, pp. 131–136 (1988)

  30. Papadopoulos-Orfanos, D., Schmitt, F.: Automatic 3D digitization using a laser rangefinder with a small field of view. In: Int. Conf. on Recent Advances in 3D Digital Imaging and Modeling, Ottawa, pp. 60–67 (1997)

  31. Pito R. (1999). A solution to the next best view problem for automated surface acquisition. IEEE Trans. PAMI 21(10): 1016–1030

    Google Scholar 

  32. Prieto F., Lepage R., Boulanger P. and Redarce T. (2003). A CAD-based 3D data acquisition strategy for inspection. Mach. Vis. Appl. 15: 76–91

    Article  Google Scholar 

  33. Reed M. and Allen P. (1999). 3D modeling from range imagery. Image Vis. Comput. 17(1): 99–111

    Article  Google Scholar 

  34. Reed M. and Allen P. (2000). Constraint-based sensor planning for scene modeling. IEEE Trans. PAMI 22(12): 1460–1467

    Google Scholar 

  35. Roth, G., Wibowo, W.: An efficient volumetric method for building closed triangular meshes from 3D image and point data. In: Proc. Graphics Interface Conf. 97 (1997)

  36. Roy S.D., Chaudhury S. and Banerjeee S. (2004). Active recognition through next view planning: a survey. Patern Recogn. 37: 429–446

    Article  Google Scholar 

  37. Scott, W.: Performance-oriented view planning for automated object reconstruction. Ph.D. thesis, University of Ottawa (2002)

  38. Scott, W.: Pose error compensation for performance-oriented view planning. Tech. Rep. NRC-47175, National Research Council of Canada, Institute for Information Technology, http://iit-iti.nrc-cnrc.gc.ca/publications/nrc-47175_e.html (2004)

  39. Scott, W.: Probing view plan solution space. Tech. Rep. NRC-47447, National Research Council of Canada, Institute for Information Technology, http://iit-iti.nrc-cnrc.gc.ca/publications/nrc-47447_e.html (2005)

  40. Scott, W., Roth, G., Rivest, J.F.: View planning for multi-stage object reconstruction. In: 14th Int. Conf. on Vision Interface, Ottawa, pp. 64–71 (2001)

  41. Scott, W., Roth, G., Rivest, J.F.: View planning with a registration constraint. In: 3rd Int. Conf. on 3D Digital Imaging and Modeling, Quebec City, pp. 127–134 (2001)

  42. Scott, W., Roth, G., Rivest, J.F.: Pose error effects on range sensing. In: 15th Int. Conf. on Vision Interface, Calgary, pp. 331–338 (2002)

  43. Scott W., Roth G. and Rivest J.F. (2003). View planning for automated 3D object reconstruction and inspection. ACM Comput. Surv. 35(1): 64–96

    Article  Google Scholar 

  44. Sen, S.: Minimal cost set covering using probabilistic methods. In: ACM Sym. Applied Computing, Indianapolis, pp. 157–164 (1993)

  45. Shin Y. and Wei Y. (1992). A statistical analysis of positional errors of a multi-axis machine tool. Precis. Eng. 14(3): 139–146

    Article  Google Scholar 

  46. Soons J., Thomas F. and Schellekens P. (1992). Modeling the errors of multi-axis machines: a general methodology. Precis. Eng. 14(1): 5–19

    Article  Google Scholar 

  47. Soucy, G., Callari, F., Ferrie, F.: Uniform and complete surface coverage with a robot-mounted laser rangefinder. In: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Victoria, pp. 1682–1688 (1998)

  48. Soucy, M., Laurendeau, D.: Multi-resolution surface modeling from multiple range views. In: Proc. IEEE Conf. Vis. Pat. Rec., pp. 348–353 (1992)

  49. Tarabanis K., Allen P. and Tsai R. (1995). A survey of sensor planning in computer vision. IEEE Trans. Robot. Autom. 11(1): 86–104

    Article  Google Scholar 

  50. Tarabanis K., Tsai R. and Allen P. (1995). The MVP sensor planning system for robotic vision tasks. IEEE Trans. Robot. Autom. 11(1): 72–85

    Article  Google Scholar 

  51. Tarbox G. and Gottschlich S. (1995). Planning for complete sensor coverage in inspection. Comput. Vis. Image Understand. 61(1): 84–111

    Article  Google Scholar 

  52. Urrutia J. (2000). Art gallery and illumination problems. In: Sack, J.-R. and Urrutia, J. (eds) Handbook of Computational Geometry, pp. Elsevier, New York

    Google Scholar 

  53. Whaite P. and Ferrie F. (1997). Autonomous exploration: driven by uncertainty. IEEE Trans. PAMI 19(3): 193–205

    Google Scholar 

  54. Woo, T.: A combinatorial analysis of boundary data structure schemata. IEEE Comput. Graph. Appl. 19–27 (1985)

  55. Yuan X. (1995). A mechanism of automatic 3D object modeling. IEEE Trans. PAMI 17(3): 307–311

    Google Scholar 

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  1. Institute for Information Technology, National Research Council of Canada, Ottawa, Canada, K1A 0R6

    William R. Scott

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Scott, W.R. Model-based view planning. Machine Vision and Applications 20, 47–69 (2009). https://doi.org/10.1007/s00138-007-0110-2

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