Utilization of Low Pass Filters for the Calculation of Termination Points for 3D Fabrication Control of Pipe Spools
Publication: Computing in Civil Engineering 2021
ABSTRACT
In the context of prefabrication and modularization, termination points are defined as local coordinate systems where assemblies are either connected or constrained. These points are typically points of connection between assemblies, sub-assemblies, or modules. As such, it is critical to ensure that termination points are measured accurately. In this study, the impact of point cloud filtering as a pre-processing step for improving the accuracy of detecting termination points in point clouds is investigated. An industrial-scale experiment was conducted where 3D scans of 40 piping components were collected and analysed while being fabricated. For data collection, each piping object was scanned using a laser scanner as well as a SLAM (simultaneous localization and mapping) scanner (80 point clouds were collected in total). The components vary in their design geometry. Using a guided Hough transform, a circle fitting method was developed to find the termination points in the scanned point clouds. It was then shown that applying noise removal as a pre-processing step for the termination point calculation can substantially improve the accuracy irrespective of the source of acquisition.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Ahmed, M. F., Haas, C. T., and Haas, R. (2014). “Automatic Detection of Cylindrical Objects in Built Facilities”, Journal of computing in civil engineering, vol. 28, no. 3, pp. 4014009.
Bailey, T., and Durrant-Whyte, H. (2006). “Simultaneous localization and mapping (SLAM): part II”, IEEE robotics & automation magazine, vol. 13, no. 3, pp. 108–117.
Clarenz, U., Rumpf, M., and Telea, A. (2004). “Fairing of point based surfaces”, Proceedings Computer Graphics International, 2004. IEEE, pp. 600.
Gibb, A. G. F. (1999). Off-site Fabrication: Prefabrication, Pre-assembly and Modularisation, Whittles.
Hernandez, M., Choi, J., and Medioni, G. (2015). “Near laser-scan quality 3-D face reconstruction from a low-quality depth stream”, Image and Vision Computing, vol. 36, pp. 61–69.
Jeanclos, N., Sharif, M., Li, S. K., Kwiatek, C., and Haas, C. (2018). “Derivation of Minimum Required Model for Augmented Reality Based Stepwise Construction Assembly Control” in Advanced Computing Strategies for Engineering. Springer International Publishing, Cham, pp. 336–358.
Kwiatek, C., Sharif, M., Li, S., Haas, C., and Walbridge, S. (2019). “Impact of augmented reality and spatial cognition on assembly in construction”, Automation in construction, vol. 108, pp. 102935.
Liu, S., Chan, K., and Wang, C. C. (2011). “Iterative consolidation of unorganized point clouds”, IEEE Computer Graphics and Applications, vol. 32, no. 3, pp. 70–83.
Lozes, F., Elmoataz, A., and Lézoray, O. (2014). “Partial difference operators on weighted graphs for image processing on surfaces and point clouds”, IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3896–3909.
Lu, W., Zhang, X., and Liu, Y. (2019). “L 1-medial skeleton-based 3D point cloud model retrieval”, Multimedia Tools and Applications, vol. 78, no. 1, pp. 479–488.
Ma, S., Zhou, C., Zhang, L., Hong, W., and Tian, Y. (2014). “Depth image denoising and key points extraction for manipulation plane detection”, Proceeding of the 11th World Congress on Intelligent Control and Automation IEEE, pp. 3315.
Mills, A., Love, P. E., and Williams, P. (2009). “Defect Costs in Residential Construction”, Journal of Construction Engineering and Management, vol. 135, no. 1, pp. 12–16.
Mostafa, S., Kim, K. P., Tam, V. W. Y., and Rahnamayiezekavat, P. (2018). “Exploring the status, benefits, barriers and opportunities of using BIM for advancing prefabrication practice”, International journal of construction management, vol. 20, no. 2, pp. 146–156.
Nahangi, M., and Haas, C. T. (2016). “Skeleton-based discrepancy feedback for automated realignment of industrial assemblies”, Automation in construction, vol. 61, pp. 147–161.
Pan, R., and Taubin, G. (2016). “Automatic segmentation of point clouds from multi-view reconstruction using graph-cut”, The Visual Computer, vol. 32, no. 5, pp. 601–609.
Pauly, M., and Gross, M. (2001). “Spectral processing of point-sampled geometry”, Proceedings of the 28th annual conference on Computer graphics and interactive techniques ACM, pp. 379.
Sharif, M., Nahangi, M., Haas, C., and West, J. (2017). “Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models”, Computer-aided civil and infrastructure engineering, vol. 32, no. 11, pp. 893–908.
Zaman, F., Wong, Y. P., and Ng, B. Y. (2017). “Density-based denoising of point cloud”, 9th International Conference on Robotic, Vision, Signal Processing and Power Applications Springer, pp. 287.
Zheng, Y., Li, G., Wu, S., Liu, Y., and Gao, Y. (2017). “Guided point cloud denoising via sharp feature skeletons”, The Visual Computer, vol. 33, no. 6-8, pp. 857–867.
Information & Authors
Information
Published In
Computing in Civil Engineering 2021
Pages: 910 - 917
History
Published online: May 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.