| Keterangan |
: Up to date and accurate 3D models of industrial sites are required for different
applications like planning, documentation and training. Traditional methods for
acquiring as-built information like manual measurements by tape and tacheometry
are not only slow and cumbersome but most of the time they also fail to provide the
amount of detail required. Many industrial facilities provide a limited personnel
access because of the presence of radioactive, toxic or hazardous materials together
with an unsafe working environment, which necessitates the use of non-contact
measurement methods.
Traditional photogrammetry depends on point or line measurements from which
it is very hard to get complete CAD models without extensive manual editing
and refinement. Compared to photogrammetry laser scanning provides explicit
and dense 3D measurements. There has been a rapid increase in the speed and
accuracy of the laser scanners in the last decade, while their costs and sizes have
been continuously shrinking. All modeling tools available on the market depend
on heavy operator intervention for most of the modeling tasks. Although there are
some semi-automatic tools like plane or cylinder growing even there the operator
has to start the growing process for each primitive. Furthermore, the fitted surfaces
must be manually edited by the operator to convert them to a CAD description.
This thesis presents new methods and techniques which can be used for automatic
or efficient semiautomatic 3D modeling of existing industrial installations from
point clouds and images. The goal is to use explicit 3D information from the point
clouds to automatically detect the objects and structure present in the scene.
The detected objects are then used as targets for model based registration, which can
be automated by searching for object correspondences. To avoid manual editing
the presented techniques use models from a catalogue of commonly found CAD
objects as templates for model fitting. In the final fitting phase images are also
included to improve the quality of parameter estimation.
Segmentation is a very important step that needs to be carried out as a pre cursor
to object recognition and model fitting. We present a method for the segmentation
of the point clouds, which avoids over-segmentation while partitioning the input
data into mutually disjoint, smoothly connected regions. It uses a criterion based
on a combination of surface normal similarity and spatial connectivity, which we
call smoothness constraint. As we do not use surface curvature our algorithm is
less sensitive to noise. Moreover, there are only a few parameters which can be
adjusted to get a desired trade-off between under- and over-segmentation.
Segmentation is followed by a stage of object recognition based on a variation
of the Hough transform for automatic plane and cylinder detection in the point
clouds. For plane detection the Hough transform is three dimensional. For the
cylinder detection the direct application of the Hough transform requires a 5D
Hough space, which is quite impractical because of its space and computational
complexity. To resolve this problem we present a two-step approach requiring a
2D and 3D Hough space. In the first step we detect strong hypotheses for the
cylinder orientation. The second step estimates the remaining three parameters of
the cylinder i.e. radius and position.
The problem of fitting models like planes, cylinders, spheres, cones, tori and CSG
models to point clouds is very important for data reduction. For the fitting of
CSG models this thesis presents three different methods for approximating the
orthogonal distance, which are compared based on speed and accuracy.
We also present methods for using modeled objects in individual scans as targets
for registration. As the available geometric structure is used, there is no need
to place artificial targets. We present two different methods for this purpose
called Indirect and Direct method. The Indirect method is a quick way to
get approximate values while the Direct method is then used to refine the
approximate solution. We also present techniques for automatically finding the
corresponding objects for registration of scans. The presented techniques are based
on constraint propagation which use the geometric information available from the
previously made correspondence decision to filter out the possibilities for future
correspondences.
Although point clouds are very important for the automation because of their
explicit 3D information, images provide a complementary source of information
as they contain well-defined edges of the bounded objects. We present methods
for the fitting of CSG models to a combination of point clouds and images. We also
present techniques for the specification of geometric constraints between sub-parts
of a CSG tree and their inclusion in the model estimation process. A taxonomy of
commonly encountered geometric constraints and their mathematical formulation
is also given.
We hope that the techniques presented in this thesis will lead to an improvement in
efficiency and quality of the models obtained for industrial installations from point
clouds and images |
