Towards Reverse Engineering of Industrial Site Plants
Resumo
CAD models of industrial sites are extremely important, as they provide documentation and simplify inspection, planning, modification, as well as a variety of physical and logistics simulations of the corresponding installations. Despite these clear advantages, many industrial sites do not have CAD models, or have trouble keeping them up-to-date. This is often due to the amount of effort required to create and maintain CAD models updated. Hopefully, the recent popularization of 3D scanning devices is promoting the development of reverse engineering, allowing the creation of 3D representations of real environments from point clouds. Nevertheless, point clouds extracted from industrial sites are extremely complex due to occlusions, noise, non-uniform sampling, size of the dataset, lack of sample organization, among other factors. Thus, a successful reverse engineering solution should have several desirable properties, including speed, robustness to noise, accuracy, and be able to handle point clouds in general without requiring one to fine tune their parameters to each dataset in order to work well on it. This thesis presents some initial efforts towards obtaining a robust framework for reverse engineering of industrial sites. It introduces two fast and robust algorithms for detecting, respectively, planes and cylinders in noisy unorganized point clouds. Planes and cylinders are typically the most common and largest structures found in those environments, representing walls, floors, ceilings, pipes, and ducts. We demonstrate the effectiveness of the proposed approaches by comparing their performances against the state-of-the-art solutions for plane and cylinder detection in unorganized point clouds. In these experiments, our solutions achieved the best overall accuracy using the same set of (default) parameter values for all evaluated datasets. This is in contrast to the competing techniques, for which their parameter values were individually adjusted for each combination of technique and dataset to achieve their best results in each case, demonstrating the robustness of our algorithms, which do not require fine-tuning to perform well on arbitrary point clouds. Moreover, our technique also displayed competitive speed to other state-of-art techniques, being suitable for handling large-scale point clouds. The thesis also presents a graphical user interface which allows further refinement of the detected structures, providing the user the ability to remove, merge, and semi-automatically detect planes and cylinders in point clouds.
Referências
Araujo, A. M. C. (2019). Towards Reverse Engineering of Industrial Site Plants. Master’s thesis, UFRGS, Porto Alegre, Brazil.
Araujo, A. M. C. and Oliveira, M. M. (2020a). Connectivity-based cylinder detection in unorganized point clouds. Pattern Recognition, 100:1-12. Article 107161.
Araujo, A. M. C. and Oliveira, M. M. (2020b). A robust statistics approach for plane detection in unorganized point clouds. Pattern Recognition, 100:1-12. Article 107115.
Farid, R. (2015). Region-growing planar segmentation for robot action planning. In Australasian Joint Conference on Artificial Intelligence, pages 179-191. Springer.
Li, L., Yang, F., Zhu, H., Li, D., Li, Y., and Tang, L. (2017). An improved RANSAC for 3d point cloud plane segmentation based on normal distribution transformation cells. Remote Sensing, 9(5):433.
Limberger, F. A. and Oliveira, M. M. (2015). Real-time detection of planar regions in unorganized point clouds. Pattern Recognition, 48(6):2043-2053.
Pham, T. T., Eich, M., Reid, I., and Wyeth, G. (2016). Geometrically consistent plane extraction for dense indoor 3d maps segmentation. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pages 4199-4204. IEEE.
Rousseeuw, P. J. and Croux, C. (1993). Alternatives to the median absolute deviation. Journal of the American Statistical association, 88(424):1273-1283.
Schnabel, R., Wahl, R., and Klein, R. (2007). Efficient RANSAC for point-cloud shape detection. In Computer graphics forum, volume 26, pages 214-226.
Tran, T.-T., Cao, V.-T., and Laurendeau, D. (2015). Extraction of cylinders and estimation of their parameters from point clouds. Computers & Graphics, 46:345-357.
Vo, A.-V., Truong-Hong, L., Laefer, D. F., and Bertolotto, M. (2015). Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 104:88-100.
Xu, L., Oja, E., and Kultanen, P. (1990). A new curve detection method: randomized Hough transform (rht). Pattern recognition letters, 11(5):331-338.
