The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVI-M-1-2021
28 Aug 2021
 | 28 Aug 2021


A. Masiero, A. Guarnieri, G. Tucci, and A. Vettore

Keywords: Point Cloud, Point Cloud Classification, Mobile Laser Scanning, Indoor Mobile Mapping

Abstract. 3D building modeling is becoming an important support in civil engineering, architecture and cultural heritage applications. Despite static laser scanning can be considered as the state-of-the-art in such kind of applications, mobile mapping techniques can be considered as a suitable alternative to quickly gather geospatial information. Outdoor mobile mapping can be considered as a mature technique, which takes into advantage of the Global Navigation Satellite System (GNSS)-laser scanning information fusion. Instead, indoor mobile mapping is typically more challenging: the unavailability of GNSS makes the mapping system rely either just on the inertial navigation system, or on some control points. A drift in the navigation solution, and consequently in the 3D reconstruction, is typically visible after a while in the former case, whereas the use of other surveying instruments is required in the latter.

This work aims at exploiting geometric characteristics of the buildings, such as symmetries and regularities, to reduce the drift effect in indoor mobile mapping, in particular when dealing with affordable systems. The proposed approach is based on the segmentation of the point clouds acquired with a time of flight camera (ToF), detecting in particular vertical planar surfaces. It is well known that aligning planar surfaces can be a viable way for reducing the drift in this kind of applications. Nevertheless, this paper aims also at investigating the use of geometric symmetries to such aim.

The proposed approach is tested on a case study, a building of the University of Padova, whose reconstruction was produced by an ad hoc affordable mobile mapping system, integrating low cost inertial sensors, RGB and ToF camera.