Human Assisted Fitting and Matching Primitive Objects to Sparse Point Clouds for Rapid Workspace Modeling in Construction Automation

-건설현장에서의 시공 자동화를 위한 Laser Sensor기반의 Workspace Modeling 방법에 관한 연구-

  • Published : 2004.10.01

Abstract

Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.

References

  1. F. Arman, and J. K. Aggarwal, 'Object Recognition in Dense Range Images Using a CAD System as a Model Base', in Proc. of IEEE Conference on Robotics and Automation, Cincinnati, OH, May, 1990, pp.1858-1863
  2. F. Arman, and J. K. Aggarwal, 'CAD-Based Vision: Object Recognition in Cluttered Range Images Using Recognition Strategies', Computer Vision Graphics Image Process: Image Understanding, Vol. 58, No.1, pp33-48, July 1993
  3. A. Johnson, and M. Hebert, 'Using spin images for efficient object recognition in cluttered 3D scenes,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, No.5, pp. 433-449, May 1999 https://doi.org/10.1109/34.765655
  4. Y. Kim, and C. Haas, 'A Model for Automation of Infrastructure Maintenance Using Representational Forms,' J. of Automation in Construction, Elsevier, pp.57-68, October 2000
  5. J. McLaughlin, C. Haas, K. Liapi, S.Y. Sreenivasan, and S. Kwon, 'Rapid Human-Assisted Creation of Bounding Models for Obstacle Avoidance in Construction,' in Proc. of 19th Annual ISARC, Gaithersburg, MD,Sept. 23-25, 2002
  6. X. Lebegue, J.K. Aggarwal, 'Automatic creation of architectural CAD models,' in Proc. of the CAD-Based Vision Workshop, February 1994, pp82-89
  7. T. Tsukiyama, 'Understanding man-made environments using nonstructured lighting-3D world modeling for indoor mobile robots,' Intelligent Robots and Systems, IROS '97, in Proc. of the 1997 IEEE/RSJ International Conference, Volume: 3, September 1997, pp 1250 -12577
  8. D. Simon, M. Hebert, and T. Kanade, 'Real-time 3-D pose estimation using a high-speed range sensor,' Robotics Institute, Carnegie Mellon University, CMU-Rl-TR-93-24, November 1993
  9. G. Cheok, and W Stone, 'Non-intrusive Scanning Technology for Construction Assessment', in Proc. of the 16th International Symposiumon Automation and Robotics in Construction (ISARC), Madrid, Spain, September 1999, pp.645-650
  10. A. Johnson, R. Hoffman, J. Osborn, and M. Hebert, 'A System for Semi-automatic Modeling of Complex Environments,' in Proc. of International Conference on Recent Advances in 3-D Digital Imaging and Modeling, May 1997, pp.213-220
  11. T. Feddema, and C. Little, 'Rapid World Modeling: Fitting Range Data to Geometric Primitives,' in Proc. of the IEEE International Conference on Robotics and Automation, Vol. 4, 1997, pp.2807-2812
  12. B. Sabata, and J.K. Aggarwal, 'Surface Correspondence and Motion Computation from a Pair of Range Images,' Computer Vision and Image Understanding, Vol. 63, No.2, pp.232-250, March 1996 https://doi.org/10.1006/cviu.1996.0017
  13. B. Sabata, F. Arman, and J. K. Aggarwal, 'Segmentation of 3D Range Images Using Pyramidal Data Structures,' Computer Vision Graphics Image Process: Image Understanding, Vol. 57, No.3, pp373-387, July 1993
  14. R. Hoffman, and A.K. Jain, 'Segmentation and Classification of Range Images,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 9, no. 5, pp 608-620, September 1987 https://doi.org/10.1109/TPAMI.1987.4767955
  15. A. Stentz, J. Bares, S. Singh, and P. Rowe, 'A Robotic Excavator for Autonomous Truck Loading', in Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp.1885-1893, 1998
  16. Cyra Technologies, Inc. 8000 Capwell Drive, Oakland, CA 94621, Tel: (510) 633-5009, www.cyra.com
  17. G. Cheok, R. Lipman, C. Witzgall, J. Bernal, and W.C .Stone, 'Field Demonstration of Laser Scanning for Excavation Measurement,' J. of Automation in Construction, Vol. 9, pp.463-477, 2000 https://doi.org/10.1016/S0926-5805(00)00058-3
  18. W. Stone, G. S. Cheok, R. Lipman, 'Automated Earthmoving Status Determination,' in Proc. of Robotics 2000. ASCE Conference on Robotics for Challenging Environments, Albuquerque, NM, February 28-March 2, 2000
  19. Y. Cho, C. Haas, K. Saidi, K. Liapi, and S. V. Sreenivasan, 'Rapid Local Area Modeling for Construction Automation,' in Proc. of 18th Intemational Symposium on Automation and Robotics in Construction, Krakow, September 2001
  20. Y. Cho, C. Haas, K. Liapi, and S. V. Sreenivasan, 'A Framework for Rapid Local Area Modeling for Construction Automation.' J. of Automation in Construction, 11(6), pp.629-641, 2002
  21. Occupational Safety and Health Standards - Excavation Final Rule, Occupational Safety and Health Admin., Fed. Register, U.S. Dept of Labor, vol. 54, p 209, Washington, D.C., 1990
  22. C. Haas, M. Skibniewski, and E. Bundy, 'History of Robotics in Civil Engineering,' J. of Microcomputers in Civil Engineering, Vol. 10(5), pp371-381, 1995
  23. A. Johnson, O. Carmichael, D. Huber, and M. Hebert, 'Toward a General 3D Matching Engine: Multiple Models, Complex Scenes, and Efficient Data Filtering,' in Proc. of the Image Understanding Workshop, Monterey, 1998, pp 1097-1107
  24. C. Eberst, et al., 'Robust Video-Based Object Recognition Integrating Highly Redundant Cues for Indexing and Verification' in Proc. of IEEE Int'I Conf. on Robotics and Automation, -3764, April 2000, pp37-57
  25. DistoMemo, Leica Geosystems Inc., CH-9435 Heerbrugg, Switzerland, Tel: 41 71 727 3131, www.leica-geosystems.com
  26. R. Duda, P. Hart, D. Stork, 'Pattern Classification,' Wliey-Interscience; 2nd edition, October, 2000
  27. X. Lebegue, J. K. Aggarwal, 'Extraction and interpretation of semantically significant line segments for a mobile robot,' in Proc. of Robotics and Automation, May 1992, pp 1778-1785
  28. B.C. Vermuri, J. K. Aggarwal, 'Representaion and Recognition of Objects from Dense Range Maps,' IEEE Trans. on Circuits and Systems, Vol. CAS-34, No. 11, November 1987
  29. B.C. Vermuri, A. Mitiche, and J. K. Aggarwal, 'Curvature-based Representation of Objects from Range Data,' Image and Vision Computing, Vol. 4, no.2, pp.107-114, 1986 https://doi.org/10.1016/0262-8856(86)90029-6
  30. D. G. Schweikert, 'An Interpolation Curve Using Spline under Tension,' J. of Math. Phys., Vol 45, pp312-317, 1966