As-built modeling of piping system from terrestrial laser-scanned point clouds using normal-based region growing

Abstract

Recently, renovations of plant equipment have been more frequent because of the shortened lifespans of the products, and as-built models from large-scale laser-scamied data is expected to streamline rebuilding processes. However, the laser-scanned data of an existing plant has an enormous amount ofpoints, captures inmcate objects, and includes a high noise level, so the manual reconstmction of a 3D model is very time-consuming and costly. Among plant equipment, piping systems account for the greatest proportion. Therefore, the purpose of this research was to propose an algorithm which could automatically recognize a piping system from the terrestrial laser- scanned data plant equipment. The straight pomon pipes, connecting parts, and connection relationship ofthe piping system can be recognized in this algorithm. Normal-based region growing and cylinder surface fitting can extract all possible locations ofpipes, including straight pipes, elbows, and junctions. Tracing the axes of a piping system enables the recognition of the positions of these elements and their connection relationship. Using only point clouds, the recognition algorithm can be performed in a fUlly automatic way. The algorithm was applied to large-scale scamied data of an oil rig and a chemical plant. Recognition rates of about 86%, 88%, and 71% were achieved straight pipes, elbows, andjunctions, respectively.

Finding the next-best scanner position for as-built modeling of piping systems

“…On the other hand, shown in Figure 8, in the proposed method, the scanner positions relatively diverged into different locations in the measuring space so that the occluded space occupied by the remaining unseen piping objects is minimized. Figure 9 compares the change in the recognition rate of the piping objects in our recognition system (Kawashima et al, 2014) when using the scanner positions derived from both methods. It was clear that our method achieved the better recognition rate than that of the previous one at the same number of scans.…”

“…The portions of straight pipes, elbows and junctions are recognized in a fully automatic way from the union of the measured point clouds from the aggregation of the previous scans using the object recognition method proposed by the authors (Kawashima, Kanai and Date, 2014). Figure 4 shows an example of the recognition of the piping objects of an oil rig.…”

“…Different from the former approaches, in this study, as shown in Figure 1(c), the best scanner position is found in the way that the method tries to minimize the spaces which are occluded and unseen but any piping object is likely to exist. To this end, part of the piping objects are sequentially recognized from its point clouds right after each scan using our object recognition method (Kawashima, Kanai and Date, 2014), and this recognition result is used as a clue to estimate the potential spaces which are likely to contain piping objects in the occluded spaces. And finally, a best scanner position is found so as to minimize only the potential spaces where the piping objects is likely to exist.…”

Geometrical Network Model Generation Using Point Cloud Data for Indoor Navigation

“…ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-4, 2018 ISPRS TC IV Mid-term Symposium "3D Spatial Information Science -The Engine of Change", 1-5 October 2018, Delft, The Netherlands Point clouds classified as floors include many missing areas, because there are laser scanning shadows and occlusions caused by a laser scanner and objects, such as tables and chairs. Although floors with slopes can be extracted from point clouds with region segmentation-based approaches (Kawashima et al 2014), a technical issue remains for floor extraction when point clouds include missing areas. Therefore, interpolation processing was applied for the missing areas in point clouds with outline extraction and filling holes, as shown in steps 2 to 4 in Figure 4.…”

Interactive Trunk Extraction from Forest Point Cloud

“…Recently terrestrial laser scanner has gaining much attention as its performance improves and been widely used in many applications, such as water resource investigation (Molina, 2014), as-built modelling of pipelines (Kawashima, 2014 and, and damage evaluation of concrete structures (Mizoguchi, 2013). The main advantage of the laser scanner is efficient and detailed documentation of the concerned targets.…”