%0 Journal Article %1 REMSEN2017 %A Lehtola, V. %A Kaartinen, H. %A Nüchter, A. %A Kaijaluoto, R. %A Kukko, A. %A Litkey, P. %A Honkavaara, E. %A Rosnell, T. %A Vaaja, M. %A Virtanen, J.-P. %A et al., %D 2017 %J Remote Sensing %K imported %P 796 %R 10.3390/rs9080796 %T Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods %U https://robotik.informatik.uni-wuerzburg.de/telematics/download/remotesensing2017.pdf %X 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.

@article{REMSEN2017, 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.}, added-at = {2017-11-08T15:05:44.000+0100}, author = {Lehtola, V. and Kaartinen, H. and N{\"u}chter, A. and Kaijaluoto, R. and Kukko, A. and Litkey, P. and Honkavaara, E. and Rosnell, T. and Vaaja, M. and Virtanen, J.-P. and et al.}, biburl = {https://www.bibsonomy.org/bibtex/29995c2927137388f8364c26bb9969ff2/nuechter76}, doi = {10.3390/rs9080796}, interhash = {c8851ca6b70a9d585d6355dfe2722446}, intrahash = {9995c2927137388f8364c26bb9969ff2}, journal = {Remote Sensing}, keywords = {imported}, month = {August}, pages = 796, timestamp = {2017-11-08T15:07:54.000+0100}, title = {{Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods}}, url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/remotesensing2017.pdf}, year = 2017 }