The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-5
06 Jun 2014
 | 06 Jun 2014

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

K. Kawashima, S. Yamanishi, S. Kanai, and H. Date

Keywords: View planning, Next best view problem, Terrestrial laser scanner, As-built model, Piping system, Occlusion

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.