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.
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