Seite - 14 - in Joint Austrian Computer Vision and Robotics Workshop 2020
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While research improved object recognition and
classification with deep neural nets [15] , parallel ef-
forts focused on template matching for object recog-
nition [2][5]. Template matching uses extracted ex-
ample images to find objects in new images. This
method often involves sliding-window based algo-
rithms [7], which find the template in a rectangular
subpartof the image. Templatematchingworkswell
for frontal images, but fails if the viewpoint differs
from the actual template [4]. The simplicity of this
technique still inspired new research, which is why
its performance has improved significantly over the
last10 years [6][11].
2.1.PoseEstimation
Buildingon topof the recognizedobject, it ispos-
sible to estimate the pose of the object relative to the
camera. This process is called pose estimation and it
consists of three general categories. In the first cat-
egory, the object’s pose is stored alongside its fea-
ture vectors. Consequently, each different observed
orientation representsa separatedetection,which re-
sults inautomaticallyknowingtheobjectspose if the
object ismatchedwithapreviously trainedone.
The second category uses statistical techniques to
align two given RGB-D images with each other. For
this, Iterative Closest Point [3], or ICP, is the most
commonly used algorithm and many variants exist
fordifferent applications [12][14].
The third category tries to combine the pose esti-
mation step with the recognition process itself. This
makessense, since,asstatedearlier,adifferentview-
pointcanchangetheappearanceofanobjectentirely.
This category has been covered by recent research
due to theemergingfieldofmachine learning [18].
Unfortunately, all of the before shown methods
need either vast amounts of training data or an ac-
curate model of the object that has to be detected. In
this work, a different approach is taken. The prin-
cipal component analysis (PCA) [1] is used for esti-
mating the pose of a known object. PCA’s intended
purpose is to extract principal components and re-
ducedimensionalitybetweentheinputandtheoutput
space. Using PCA to estimate the pose of the object,
the needed input to the algorithm can be reduced to
only the template. The proposed process of pose es-
timation withPCAis shown in thenext chapter. Z X
Y
Transformation
Camera Origin GraspingArea
X
Z
Y
Figure 1. Visualization of the grasping point. The figure
shows the object that has to be grasped. The orange coor-
dinate system shows thecenterof thegraspingarea.
3.METHODS
The objective of the proposed approach is the es-
timation of the pose of a known object. Before the
pose of an object can be calculated, it first has to be
located in an image. For this task, template match-
ingwaschosendueto itseaseof implementationand
use. After theobjecthasbeenrecognizedinthedepth
image,principalcomponentanalysis isusedtodeter-
mine the orientation of the found subpart of the im-
age in3Dspace.
Figure1shows the targetobjectof thiswork. The
pose of the shape in the “grabbing area” has to be
calculated so that it can be successfully grasped. For
this, thenormalvectorof thesurface facing thecam-
era has to be found. Through orientation of the vec-
tors the rotational components of the 6D pose can
be determined. This task can be solved by comput-
ing the PCA for the points in the grabbing area. In
this case, the principal component analysis yields 3
eigenvectorswith their respectiveeigenvalues for the
given 3D points. As can be seen by studying Figure
1, 2 of the 3 dimensions of the shape in the grab-
bingareadiffer fromtheother. Thespanofvalues in
the X and Y direction are comparatively large in re-
spect to the depth dimension Z. This also applies to
the respective variances. Using prior knowledge, the
normalvectorof theplaneparallel to thecameraori-
gin (i.e. corresponding to the surface of the marked
grabbing area) can be estimated using the eigenvec-
14
Joint Austrian Computer Vision and Robotics Workshop 2020
- Titel
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Herausgeber
- Graz University of Technology
- Ort
- Graz
- Datum
- 2020
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Kategorien
- Informatik
- Technik