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AutonomousGraspingofKnownObjectsUsingDepthDataandthePCA
DominikSteigl,MohamedAburaia,WilfriedWo¨ber
UASTechnikumWien
{mr18m007,mohamed.aburaia,wilfried.woeber}@technikum-wien.at
Abstract.Two main goals for automated object ma-
nipulation processes are cost reduction and flexibil-
ity. Time-consuming, costly object-specific fixtures
can be replaced by vision systems, whereby the ma-
nipulators are extended with cameras so that multi-
ple objects in the environment can be precisely iden-
tified. To be able to manipulate an object, it must be
recognized first in the world, and then the pose must
becalculated. Neuralnetworkapproachesrecognize
and estimate the pose of an object in a single step
and yield superior results, but rely on vast amounts
of training data. This work describes an approach
for estimating the pose of identified objects without
pre-trained pose data. Template matching is used
to recognize objects in depth images, and the pose
is estimated through principal component analysis
(PCA). The input to the algorithm is reduced to the
template. Pre-existing knowledge about the object
further improves accuracy. A maximum deviation
of 0.2 cm from the ground truth has been achieved,
which suffices for the industrial grasping task. The
system was evaluated with real measurements taken
with an RGB-D camera. This work resembles a first
step to estimate an object’s pose with linear statisti-
calmethods.
1. INTRODUCTION
Industrial robotsareefficientatpickingupobjects
in a predefined, structured environment [10]. When
mobile manipulators are deployed in a factory set-
ting and costly fixtures have to be avoided, robots
need theability to identifyand locateobjects forma-
nipulation. To overcome this problem, a vision sys-
tem can be used. One way to give robot vision is to
use two-dimensional imageswithdepth information,
alsoknownas2.5DimagesorRGB-Dimages. RGB-
D images can be used to find and localize objects
by analyzing the environment. Building on top of the recognized and classified object, pose estimation
tries to estimate the six degrees of freedom (DOF)
pose of an object in an image. For mobile manipu-
lation of objects this information is needed to accu-
ratelygraspobjectswithamanipulator in thecorrect
positionandorientation.
The current state of the art approaches towards
object recognition and pose estimation are based on
deep neural networks [15]. They usually outperform
human crafted features [19], but unfortunately they
rely on huge amounts of training data for classifica-
tionandposeestimationandaredifficult toadapt[9].
This iswhy, in thiswork,amoretraditionalapproach
was chosen. The target object is recognized using
template matching in a 3D space. Pose estimation is
implementedusing theprincipal componentanalysis
(PCA) to place an orthogonal basis in the center of
thegrabbingarea. UsingPCAtoestimate theposeof
the object, the needed input to the algorithm can be
reduced to only the template. This work resembles
a first step to estimate an object’s pose with linear
statisticalmethods.
In the following chapters the related work is sum-
marized, the used methods are explained and the re-
sults arebeingdiscussed.
2.RELATEDWORK
Object recognition describes the task of localiz-
ing known objects in images. Due to changes in the
viewpoint or lighting, the task of mapping the huge
amountof inputpixels toasmalloutput space is still
complex [16]. To mitigate the influence of lighting
conditions, approaches which rely on 3D informa-
tion were researched [8]. The data used in these ap-
proaches is usually made up of a three channel 8-bit
RBG image or an additional fourth channel which
represents the 3D distance of the object to the image
sensor,whereeach image isdescribedusing
13
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik