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mation [30]. Open source implementations for dif-
ferent learning tasks are plentiful and can be used
to provide perception for a robotics system. Due to
thestrongcapabilitiesofCNNsasgeneralfeatureex-
tractors, it is possible to learn multiple visual targets
which can be different depending on the environ-
mentorapplication. Thisrelaxestheconstraintofus-
ingspecificallydesignedvisualmarkersthatclassical
CVmethodspose. Thelearningtaskof theobjectde-
tector in thiswork iscomparativelysimple (onlyone
class of logos exist and they are easily distinguish-
able fromthe restof the target, seeFig.1).
In previous work the LiDAR of the mobile robot,
a robotino, was used to create a map of the envi-
ronment and localization was implemented with the
amcl package. While this pipeline in combination
with obstacle avoidance methods has been useful for
path-planning across the room, only employing the
AMCL the robot arrives at the target position with
great inaccuracy (10cmto20cm). Therefore, for this
project an entirely vision-based solution for docking
was developed which is bound to take over the task
of generating pose-estimates from the AMCL once
the robot comesclose to thedocking target.
The aim of this work therefore is to approach and
dockontodesiredtargetsinasemiindustrialenviron-
ment with sufficiently high accuracy. To contribute
to the transitionofstate-of-the-artCNNsfrompublic
datasets to real world problems an appropriate com-
bination of old and new algorithms is presented in
this work. A CNN based object detectors is used
for image processing and object detection, followed
by a camera pose estimation algorithm using point
correspondences from the detections. The presented
method could be easily adapted to learn new target
positionsoutfittedwithavisualmarkerwithminimal
setup requirements.
2.STATEOFTHEART
Theproblemofestimating theposeofacalibrated
camera,assumingaknown3Dscene, isknownasthe
PnP-problem[29]. The idea is tousea featuredetec-
tor suchasSIFT[16]orSURF[2] toextract features
from multiple sequential images. Since an image of
aknown3Dpointgives twononlinearconstraintson
camera pose and calibration, using three points (or
moreprecisely three image-objectpointpairs)would
give all 6 pose parameters. As [33] point out, such
minimal cases lead to polynomial systems with mul-
tiple solutions, hence one additional point is used. This leads to fournecessarypoints forestimating the
pose (and one intrinsic parameter) and six points for
estimation of 3D pose and five additional calibration
parameters. The problem is formulated diffently for
theplanar two-dimensionalor thegeneral,aforemen-
tioned three dimensional case. Direct Linear Trans-
formation(DLT, [9])allows theestimationof theho-
mography matrixH for the planar problem, requir-
ing at least four 2D-3D point correspondences. For
the general case, DLT estimates the projection ma-
trixP andrequiresat leastsixsuchcorrespondences.
In either case,H orP can be expressed with a set
A~x=0of multiple pairs of independent equations.
Since individual pixels are generally noisy, no exact
solution can be obtained using DLT, only an approx-
imate solution by obtaining the SVD ofA. It should
benoted, that for thenoisyandoverconstrainedcase,
only the eigenvector ofATA, corresponding to the
smallest eigenvalue, should be computed. A contin-
uation to DLT is the family of PnP algorithms. Effi-
cient PnP or EPnP [14] uses the notion that each of
then 3D-2D point pairs are expressed as weighted
sum of four virtual control points, and solves the
poseproblemfromthesecontrolpoints. Perspective-
Three-Point or P3P is a method applicable if only
three correspondences are obtained, and in turn re-
turns four real, possible solutions, the newest imple-
mentation being Lambda Twist P3P [25]. A fourth
point pair can be used to remove this four-solution
ambiguity.
Kartoun et al. [12] were able to achieve docking
timesaveraging85secondsbutattributedthesuccess
of their method to the unique hardware on the robot
and a generously large docking station. Burschka et
al. [3] take the aforementioned approach to the out-
doors, using a Kanade-Lucas tracker [18] to track
points in image sequences, followed by RANSAC
and DLT. They achieve good results for rotation, but
struggle with estimating translation. In the work
of Mehralian et al. [21] an Extended Kalman Filter
(EKF, [11]) is combined with PnP algorithms to cre-
ate EKFPnP. They achieve better robustness against
noisy features, although no details are given regard-
ing the feature tracker.
In the field of deep learning, pose estimation is
awell researchedproblem[23], cameraposeestima-
tionislessso[13]andnoarchitecturesordatasetsex-
ists specificallydesignedfordockingamobile robot.
Thedatasetwouldneed to include thecompletepose
of the robot for every captured image to allow end-
7
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