Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

Abstract

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning

“…Simultaneous Localisation and Mapping (SLAM) is a technique that can be used to generate the point cloud in indoor environments. The point clouds of five selected commercial systems, Matterport [25], Zebedee [26], NavVis [27], Leica Pegasus: Backpack [28], and Kaarta Stencil [29], and three research prototypes, Aalto VILMA [30,31], FGI Slammer [2], and the Würzburg backpack [32], are compared with survey-grade terrestrial laser scanning (TLS) point clouds obtained with Leica and Faro scanners (i.e., the "truth") in [33], where an agreement of 0.2 m-2 m was found. SLAM-based methods do not need control points (i.e., points with known coordinates) in point cloud generation; however, these methods will need control points for georeferencing the points in point clouds at a later stage if the 3D models are required to tie in with national coordinates systems, which is always the case when point clouds are used for BIM and GIS applications.…”

Hybrid processing of laser scanning data

“…The well-known features of laser scanning data are high accuracy and density and as a result of which big spatial point cloud data are created [15]. The process of obtaining point cloud for each type of laser scanning is well explored and the appropriate software have been developed by providers [16].…”

Global Registration of 3D LiDAR Point Clouds Based on Scene Features: Application to Structured Environments

“…The correspondences between points were established searching for the nearest neighbor of each target point in the source after transformation. To deal with the overlap issue in this association, a threshold was chosen to reject outliers in the matching pairs as suggested by Lehtola et al in their study about the evaluation of point clouds' reconstruction [45]. However, in our case, this threshold is set as the resolution of the point cloud.…”