Page - 6 - in Joint Austrian Computer Vision and Robotics Workshop 2020
Image of the Page - 6 -
Text of the Page - 6 -
Vision-basedDockingofaMobileRobot
AndreasKriegler,WilfriedWo¨ber
UASTechnikumVienna
{mr18m016,woeber}@technikum-wien.at
Abstract. For mobile robots to be considered au-
tonomous they must reach target locations in re-
quiredpose,aprocedurereferredtoasdocking. Pop-
ular current solutions use LiDARs combined with
sizeable docking stations but these systems struggle
by incorrectly detecting dynamic obstacles. This pa-
per instead proposes a vision-based framework for
docking a mobile robot. Faster R-CNN is used for
detecting arbitrary visual markers. The pose of the
robot is estimated using the solvePnP algorithm re-
lating 2D-3D point pairs. Following exhaustive ex-
periments, it is shownthat solvePnPgivessystemati-
cally inaccurateposeestimates in thex-axispointing
to the side. Pose estimates are off by ten to fifty cen-
timeters and could therefore not be used for docking
therobot. Insightsareprovidedtocircumventsimilar
problems in futureapplications.
1. INTRODUCTION
Dockingcanbeunderstoodas the localizationand
navigation of a robot towards a target location [1].
In contrast to path-planning across larger distances,
docking does not require obstacle avoidance meth-
ods but instead seeks highly accurate pose estimates
[28]. As long as the pose of the robot and the target
location are known in a reference coordinate system
path planning algorithms can easily generate control
commands. In thexy ground-plane, the pose~x con-
sists of three degrees of freedom,x, y, and θ as the
rotation about its own axis z, and is described using
the stateat time t
~xt= (
x x˙ y y˙ θ θ˙ )T
t (1)
where x˙, y˙ and θ˙ describe the speed of the robot in
x and y and its rotation respectively. As Thrun et
al. [32] write outlining the motion model and mea-
surement model, taking multiple control steps ~ut
with only an initial measurement or observation ~zt Figure 1. The visual target used for docking. The target
location is on the ground infront. The origin for the PnP
solvers is in the upper left corner. The logos are roughly
9x3 centimeters in size. The upper right logo was raised
duringexperiments to removecoplanarity.
leads to large uncertainties about its pose, they pro-
pose a measurement step after every control to re-
storeconfidence in thebeliefbel(~x). Thesemeasure-
ments can be non-vision methods such as evaluat-
ing detections from LiDAR-scans [22] or can come
from a camera setup providing visual feedback [6].
Yurtsever et al. [34] show in their survey on auto-
mated driving systems (ADS) that computer vision
(CV) based approaches to navigation have become
increasingly popular. Artificial landmark detection
as described by Luo et al. [19] and gradient based
optical flow [20] rival modern non-vision solutions.
Classical non-vision systems typically employ Li-
DARtechnology, indoorGPSorwirelessfingerprint-
ing [17]. While LiDARs are still widely used com-
mercially (such as MiRs and Robotinos) recent ad-
vances in deep learning and their application in the
ADS domain are of more scientific interest. Deep
ConvolutionalNeuralNetworks(CNNs)haveproven
successful at tackling a variety of perception prob-
lems, including object detection [26] and pose esti-
6
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