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Automated building and evaluation of 2D as-built floor plans

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Abstract

Site inspection is a notably tedious, time-consuming, and error-prone process when carried out manually by construction inspectors. One portion of site inspection is the generation and comparison of as-built drawings to their as-planned counterparts to ensure conformity. Current as-built evaluation systems rely on manually positioning and transporting scanning stations around the site to collect a cohesive map of the environment and then followed by a manual assessment of the site status using the built drawings. This paper proposes an automated alternative to the generation and assessment of 2D as-built floor maps by relying on robotic simultaneous localisation and mapping (SLAM) to build the 2D as-built maps, followed by automated evaluation of the as-built maps using machine vision. The components of the proposed system have been tested through proof of concept experiments inside 2 different constructed sites. Results indicate average errors below 4 cm (site of 30 m x 15 m) between the disparities identified by our proposed system versus ground truth measured disparities.

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References

  1. Adán, A., Quintana, B., Prieto, S., Bosché, F.: An autonomous robotic platform for automatic extraction of detailed semantic models of buildings. Autom. Constr. 109, 102963 (2020)

    Article  Google Scholar 

  2. Asadi, K., Ramshankar, H., Noghabaei, M., Han, K.: Real-time image localization and registration with bim using perspective alignment for indoor monitoring of construction. J. Comput. Civ. Eng. 33(5), 04019031 (2019)

    Article  Google Scholar 

  3. Bosché, F.: Automated recognition of 3D cad model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv. Eng. Inform. 24(1), 107–118 (2010)

    Article  Google Scholar 

  4. Boukamp, F., Akinci, B.: Automated processing of construction specifications to support inspection and quality control. Autom. Constr. 17, 90–106 (2007)

    Article  Google Scholar 

  5. Brian Gerkey ROS Wiki: gmapping. https://wiki.ros.org/gmapping (2007). Accessed 4 Oct 2019

  6. Canny, J.: A computational approach to edge detection. In: Readings in computer vision, pp. 184–203. Elsevier (1987)

  7. Cha, H.S., Jiang, S.: Special issue on bim in the construction industry. Appl. Sci. (2020). https://doi.org/10.3390/app10124306

    Article  Google Scholar 

  8. Durrant-Whyte, H., Bailey, T.: Simultaneous localisation and mapping: part 1. Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  9. Everett, J.: Back to basics in construction automation. In: International Association for Automation and Robotics in Construction, pp. 583–590 (1990)

  10. Felzenszwalb, P.F., Huttenlocher, D.P.: Distance transforms of sampled functions. Theory Comput. 8(1), 415–428 (2012)

    Article  MathSciNet  Google Scholar 

  11. Feng, C., Xiao, Y., Willette, A., McGee, W., Kamat, V.R.: Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites. Autom. Constr. 59, 128–138 (2015)

    Article  Google Scholar 

  12. Filatov, A., Filatov, A., Krinkin, K., Chen, B., Molodan, D.: 2D slam quality evaluation methods. In: 2017 21st Conference of Open Innovations Association (FRUCT), pp. 120–126. IEEE (2017)

  13. Filipenko, M., Afanasyev, I.: Comparison of various slam systems for mobile robot in an indoor environment. In: International Conference on Intelligent Systems (2018)

  14. Gallaher, M.P., O’Connor, A.C., Dettbarn, J.L.J., Gilday, L.T.: Cost analysis of inadequate interoperability in the u.s. capital facilities industry. Technical report, National Institute of Standards and Technology (2004)

  15. Garrido, G.G.: Development of a tightly-coupled composite vision/laser sensor for indoor slam. Ph.D. thesis, École Nationale Supérieure des Mines de Paris (2011)

  16. Gholami Shahbandi, S., Magnusson, M.: 2d map alignment with region decomposition. Autonomous Robots (2017). https://doi.org/10.1007/s10514-018-9785-7

  17. Grisetti, G., Stachniss, C., Burgard, W., et al.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Rob. 23(1), 34 (2007)

    Article  Google Scholar 

  18. Hähnel, D., Burgard, W., Thrun, S.: Learning compact 3d models of indoor and outdoor environments with a mobile robot. Robot. Auton. Syst. 44, 15–27 (2003)

    Article  Google Scholar 

  19. Hamledari, H., Davari, S., Azar, E.R., McCabe, B., Flager, F., Fischer, M.: Uav-enabled site-to-bim automation: Aerial robotic-and computer vision-based development of as-built/as-is bims and quality control. In: Construction research congress, pp. 336–346 (2017)

  20. Han, K., Degol, J., Golparvar-Fard, M.: Geometry-and appearance-based reasoning of construction progress monitoring. J. Constr. Eng. Manag. 144(2), 04017110 (2018)

    Article  Google Scholar 

  21. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)

  22. Hilti: Laser meters. https://www.hilti.com/c/CLS_MEA_TOOL_INSERT_7127/CLS_LASER_METERS_7127. Accessed 4 Oct 2019

  23. Jiang, G., Yin, L., Jin, S., Tian, C., Ma, X., Ou, Y.: A simultaneous localization and mapping (slam) framework for 2.5d map building based on low-cost lidar and vision fusion. Appl. Sci. (2019). https://doi.org/10.3390/app9102105

    Article  Google Scholar 

  24. Jung, J., Hong, S., Yoon, S., Kim, J., Heo, J.: Automated 3D wireframe modeling of indoor structures from point clouds using constrained least-squares adjustment for as-built bim. J. Comput. Civ. Eng. 30(4), 04015074 (2015)

    Article  Google Scholar 

  25. Kim, Y.S., Oh, S.W., Cho, Y.K., Seo, J.W.: A pda and wireless web-integrated system for quality inspection and defect management of apartment housing projects. Autom. Constr. 17, 163–179 (2008)

    Article  Google Scholar 

  26. Klein, L., Li, N., Becerik-Gerber, B.: Imaged-based verification of as-built documentation of operational buildings. Autom. Constr. 21, 161–171 (2012)

    Article  Google Scholar 

  27. Kopsida, M., Brilakis, I.: Real-time volume-to-plane comparison for mixed reality-based progress monitoring. J. Comput. Civ. Eng. 34(4), 04020016 (2020)

    Article  Google Scholar 

  28. Kosub, S.: A note on the triangle inequality for the jaccard distance. Pattern Recogn. Lett. 120, 36–38 (2019)

    Article  Google Scholar 

  29. Lakaemper, R., Madhavan, R.: Towards evaluating world modeling for autonomous navigation in unstructured and dynamic environments. In: Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop, pp. 355–360 (2010)

  30. Macher, H., Landes, T., Grussenmeyer, P.: From point clouds to building information models: 3D semi-automatic reconstruction of indoors of existing buildings. Appl. Sci. 7(10), 1030 (2017)

    Article  Google Scholar 

  31. Meng, Z., Wang, C., Han, Z., Ma, Z.: Research on slam navigation of wheeled mobile robot based on ros. In: 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), pp. 110–116. IEEE (2020)

  32. Michael Schwind: Comparing Lidar and Photogrammetric Point Clouds. https://www.gim-international.com/content/article/comparing-lidar-and-photogrammetric-point-clouds. Accessed 10 Oct 2019

  33. Moravec, H.P.: Sensor fusion in certainty grids for mobile robots. In: Sensor Devices and Systems for Robotics, pp. 253–276. Springer (1989)

  34. Moreno, C., Olbina, S., Issa, R.: Bim use by architecture, engineering, and construction (aec) industry in educational facility projects. Adv. Civil Eng. 2019, 1–19 (2019). https://doi.org/10.1155/2019/1392684

    Article  Google Scholar 

  35. Paterson, A.M., Dowling, G.R., Chamberlain, D.A.: Building inspection: Can computer vision help? Autom. Constr. 7, 13–20 (1997)

    Article  Google Scholar 

  36. Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., Haas, C.: State of research in automatic as-built modelling. Adv. Eng. Inform. 29(2), 162–171 (2015)

    Article  Google Scholar 

  37. Sanhudo, L., Ramos, N.M., Martins, J.P., Almeida, R.M., Barreira, E., Simões, M.L., Cardoso, V.: Building information modeling for energy retrofitting-a review. Renew. Sustain. Energy Rev. 89, 249–260 (2018)

    Article  Google Scholar 

  38. Santos, J.M., Portugal, D., Rocha, R.P.: An evaluation of 2d slam techniques available in robot operating system. In: 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6. IEEE (2013)

  39. Shahbandi, S.G., Magnusson, M., Iagnemma, K.: Nonlinear optimization of multimodal two-dimensional map alignment with application to prior knowledge transfer. IEEE Robot. Autom. Lett. 3(3), 2040–2047 (2018)

    Article  Google Scholar 

  40. Shi, J., Tomasi, C.: Good features to track. Technical Teport, Cornell University (1993)

  41. Son, H., Na, J., Kim, C.: Semantic as-built 3d modeling of buildings under construction from laser-scan data based on local convexity without an as-planned model. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 32, p. 1. IAARC Publications (2015)

  42. SRA, B.M.: The ansi z765 standard for calculating square footage. Apprais. J. 81(4), 300 (2013)

    Google Scholar 

  43. Stanford Artificial Intelligence Laboratory et al.: Robotic operating system. https://www.ros.org (2018)

  44. Thomas, B.: Icwgb. http://www.icwgb.org/ (2012)

  45. Thrun, S.: Learning occupancy grid maps with forward sensor models. Auton. Robot. 15, 111–127 (2003)

    Article  Google Scholar 

  46. Thrun, S., Bucken, A.: Integrating grid-based and topological maps for mobile robot navigation. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 944–951 (1996)

  47. Tingguang, L., Danny, H., Chenming, L., Delong, Z., Chaoqun, W., Meng, M.Q.H.: Houseexpo: A large-scale 2d indoor layout dataset for learning-based algorithms on mobile robots. arXiv preprint arXiv:1903.09845 (2019)

  48. Tutsoy, O., Barkana, D.E.: Model free adaptive control of the under-actuated robot manipulator with the chaotic dynamics. ISA Transactions (2021)

  49. Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 4, 376–380 (1991)

    Article  Google Scholar 

  50. Wang, B., Yin, C., Luo, H., Cheng, J.C., Wang, Q.: Fully automated generation of parametric bim for mep scenes based on terrestrial laser scanning data. Autom. Constr. 125, 103615 (2021)

    Article  Google Scholar 

  51. Wei, Y., Akinci, B.: A vision and learning-based indoor localization and semantic mapping framework for facility operations and management. Autom. Constr. 107, 102915 (2019)

    Article  Google Scholar 

  52. Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3D building models form laser scanner data. Autom. Constr. 31, 325–337 (2013)

    Article  Google Scholar 

  53. Yagfarov, R., Ivanou, M., Afanasyev, I.: Map comparison of lidar-based 2D slam algorithms using precise ground truth. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1979–1983. IEEE (2018)

  54. Yoon, S., Jung, J., Heo, J.: Practical implementation of semi-automated as-built bim creation for complex indoor environments. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 40(4), 143 (2015)

    Article  Google Scholar 

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Acknowledgements

Funding the research for this publication was provided by a grant from the University Research Board (URB) at the American University of Beirut.

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Authors and Affiliations

  1. Vision and Robotics Lab, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Riad El Solh, 1107-2020, Beirut, Lebanon

    Daniel Asmar, Rema Daher, Yasmine Hawari & Imad H. Elhajj

  2. Civil and Environmental Engineering, Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Riad El Solh, 1107-2020, Beirut, Lebanon

    Hiam Khoury

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Asmar, D., Daher, R., Hawari, Y. et al. Automated building and evaluation of 2D as-built floor plans. Machine Vision and Applications 33, 36 (2022). https://doi.org/10.1007/s00138-022-01289-8

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