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.

ABSTRACT:Renovation of plant equipment of petroleum refineries or chemical factories have recently been frequent, and the demand for 3D asbuilt modelling of piping systems is increasing rapidly. Terrestrial laser scanners are used very often in the measurement for as-built modelling. However, the tangled structures of the piping systems results in complex occluded areas, and these areas must be captured from different scanner positions. For efficient and exhaustive measurement of the piping system, the scanner should be placed at optimum positions where the occluded parts of the piping system are captured as much as possible in less scans. However, this "nextbest" scanner positions are usually determined by experienced operators, and there is no guarantee that these positions fulfil the optimum condition. Therefore, this paper proposes a computer-aided method of the optimal sequential view planning for object recognition in plant piping systems using a terrestrial laser scanner. In the method, a sequence of next-best positions of a terrestrial laser scanner specialized for as-built modelling of piping systems can be found without any a priori information of piping objects. Different from the conventional approaches for the next-best-view (NBV) problem, in the proposed method, piping objects in the measured point clouds are recognized right after an every scan, local occluded spaces occupied by the unseen piping systems are then estimated, and the best scanner position can be found so as to minimize these local occluded spaces. The simulation results show that our proposed method outperforms a conventional approach in recognition accuracy, efficiency and computational time.

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ABSTRACT:Recently, changes in plant equipment have been becoming more frequent because of the short lifetime of the products, and constructing 3D shape models of existing plants (as-built models) from large-scale laser scanned data is expected to make their rebuilding processes more efficient. However, the laser scanned data of the existing plant has massive points, captures tangled objects and includes a large amount of noises, so that the manual reconstruction of a 3D model is very time-consuming and costs a lot. Piping systems especially, account for the greatest proportion of plant equipment. Therefore, the purpose of this research was to propose an algorithm which can automatically recognize a piping system from terrestrial laser scan data of the plant equipment. The straight portion of pipes, connecting parts and connection relationship of the piping system can be recognized in this algorithm. Eigenvalue analysis of the point clouds and of the normal vectors allows for the recognition. Using only point clouds, the recognition algorithm can be applied to registered point clouds and can be performed in a fully automatic way. The preliminary results of the recognition for large-scale scanned data from an oil rig plant have shown the effectiveness of the algorithm.

ABSTRACT:In recent years, renovations of plant equipment have been more frequent, and constructing 3D as-built models of existing plants from large-scale laser scanned data is expected to make rebuilding processes more efficient. However, laser scanned data consists of enormous number of points, captures tangled objects and includes a high noise level, so that the manual reconstruction of a 3D model is very time-consuming. Among plant equipment, piping systems especially account for the greatest proportion. Therefore, the purpose of this research was to propose an algorithm which can automatically recognize a piping system from large-scale laser scanned data of plants. The straight portion of pipes, connecting parts and connection relationship of the piping system can be automatically recognized. Normal-based region growing enables the extraction of points on the piping system. Eigen analysis of the normal tensor and cylinder surface fitting allows the algorithm to recognize portions of straight pipes. Tracing the axes of the piping system and interpolation of the axes can derive connecting parts and connection relationships between elements of the piping system. The algorithm was applied to large-scale scanned data of an oil rig and a chemical plant. The recognition rate of straight pipes, elbows, junctions achieved 93%, 88% and 87% respectively. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.