Seite - 54 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics
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• Path extraction: A path must be computed which
guides the vehicle between the traffic cones to the
destination and marks the drivable area.
• Motioncontrol:Themotioncontrolleruses thedrivable
area detected between the cones, computes an optimal
trajectory, and dispatches the actual motion commands
for steering and accelerating the car.
In Fig. 4 one can see an overview of the messaging system
among the ROS components. It shows the connection of
the actuators to the sensors, as well as the processing steps
needed to achieve desired control sequences. Some of them
are implemented as nodelets to minimise messaging delays.
In the following section techniques used are described in
more detail.
IV. APPROACH
The EKF-SLAM implementation used is based on Mac-
sek’s [6] Master’s thesis. The filter uses perception of cone
measurements with uncertainties and generates a map of
the cones in 2D. It also determines the car’s own position
on the map, computes its position relative to previous laps,
and performs motion control. It is necessary to predict the
vehicle’s pose ahead of time in order to send proper motion
commands, since actuators have a considerable reaction
delay.
For sensing, it is vital to detect traffic cones in the environ-
ment and estimate their location relative to the sensors. The
known location of the sensors on the vehicle enables one to
create a global map of landmarks for the trajectory-planning
algorithm.
In the first development phase, TUW Racing used Pio-
neer 3AT in a simulated and real world scenario in order to
generate measurements, try out sensors and plan algorithms,
as shown in Fig. 5.
A. Traffic cone detection
Becauseof thevarietyof sensorsused (lasersandcameras)
various techniques have to be implemented accordingly. Two
approaches are used to perform cone detection and position
estimation on the stereo camera images. In the first approach,
a depth image based on a block-matching algorithm is used.
This allows for extraction and removal of planes in a 3D
data set. The track floor that is geometrically known can
be pre-segmented, and cone candidates appear as isolated
objects. These candidates are then further evaluated using
classical image processing steps on the estimated location
in the image plane, similar to the approach of Yong and
Jianru [11]. However, a block matching algorithm cannot be
easily adapted to use the known structure of the environment
for improved perception of target objects. Moreover, the
computation effort and thus hardware requirements would
be unnecessarily high, since one would have to operate on
an entire image.
In the second approach, cones can be detected in both
images separately. The algorithm computes the disparity, the
z-depth and consecutively the 3D position of the cone, based only on the UV center points of the same object determined
in the left and right images.
In both approaches, the program determines the colors
heuristically with RGB color thresholds. If the estimated
center point is within a white or a black stripe, the color
sample point is moved downwards until another color match
is determined.
In addition, the planar laser scan is searched for point
clusters of distances that match the radius of the cone at
the height scanned. If the total distance spanned by coherent
scan points is above this radius but below the total radius
of the cone base, the algorithm considers the center point
between the cluster to be the center point of a cone. In order
to avoid detection of obstacles other than cones, the detector
filters long connected components such as walls.
The detection results of both algorithms are then fused
with a feature-based EKF-SLAM [6]. Thus the team benefits
from the advantages of both of the detection approaches,
because the fusion algorithm respects the reliability of each
detection result and takes the more reliable detection result
intogreateraccount.Forexample,distanceestimationathigh
distances is less accurate at image-based detection, due to the
fact that pixel disparity is smaller. On the other hand, laser-
based detection cannot reliably detect cones that are behind
other cones.
B. Mapping
For TUW Racing, it is essential that the mapping com-
ponents be able handle failures in the detection component.
The camera or the laser could have short periods of blindness
due to sunlight or uneven road conditions. In these cases a
cone might be missed. Since cones are guaranteed to be at
most 5 m apart, missing ones can be filled in. Because all
cones on the left side have a different color than those on
the right, heuristics can fill in cones missing from one side
based on the cones observed on the other side.
Since the trafficcones themselvesare thebest reference for
position and orientation on the track, the team uses an EKF-
SLAM that maps them and corrects the vehicle’s position
based on their perceived change of position. An additional
challenge here is that the same track sections are driven ten
times throughout the competition. Thus, the mapper must
recognize previously mapped cones by ID, even though they
are of the same color and shape. An index counter from the
starting line is insufficient due to potential perception failure.
Nor does the vehicle have a full 360deg line of sight. In a
narrow left turn, due to the vehicle’s pose in an optimal
trajectory, it is possible that only the outer cones might
be seen. If the mapping algorithm performs corrections to
positions on the map on those right lane cones, the left lane
cones must also be updated automatically.
C. Trajectory Planning
From the mapping one can derive a path which lies in the
center of the travel area; path-planning is therefore obsolete.
To start, a normal vector is projected from the center point
between the first left and right cones forward. The first cones
54
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Titel
- Proceedings of the OAGM&ARW Joint Workshop
- Untertitel
- Vision, Automation and Robotics
- Autoren
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Verlag
- Verlag der Technischen Universität Graz
- Ort
- Wien
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Schlagwörter
- Tagungsband
- Kategorien
- International
- Tagungsbände