The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 495–500, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-495-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 495–500, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-495-2020

  12 Aug 2020

12 Aug 2020

DETECTION OF INDOOR ATTACHED EQUIPMENT FROM TLS POINT CLOUDS USING PLANAR REGION BOUNDARY

H. Takahashi1, H. Date1, S. Kanai1, and K. Yasutake2 H. Takahashi et al.
  • 1Graduate School/Faculty of Information Science and Technology, Hokkaido University, 060-0814 Sapporo, Japan
  • 2Kyudenko Corporation, 815-0081 Fukuoka, Japan

Keywords: terrestrial laser scanner, point cloud, indoor scene, attached equipment, detection

Abstract. Laser scanning technology is useful to create accurate three-dimensional models of indoor environments for applications such as maintenance, inspection, renovation, and simulations. In this paper, a detection method of indoor attached equipment such as windows, lightings, and fire alarms, from TLS point clouds, is proposed. In order to make the method robust against to the lack of points of equipment surface, a footprint of the equipment is used for detection, because the entire or a part of the footprint boundary shapes explicitly appear as the boundary of base surfaces, i.e. walls for windows, and ceilings for lightings and fire alarms. In the method, first, base surface regions are extracted from given TLS point clouds of indoor environments. Then, footprint boundary points are detected from the region boundary points. Finally, target equipment is detected by fitting or voting using given target footprint shapes. The features of our method are footprint boundary point extraction considering occlusions, shape fitting with adaptive parameters based on point intervals, and robust shape detection by voting from multiple footprint boundary candidates. The effectiveness of the proposed method is evaluated using TLS point clouds.