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Fig. 4. ROS nodes and message subscriptions in rqt graph
Fig. 5. Edge8 in a simulated environment using GazeboSim.
to the left and right of this vector are identified, then the
center point identified, and a vector projected forward again.
Since the left and right sides can be distinguished, a situation
where the cut to the left of the vector which contains a right-
side cone, or vice-versa, is manageable. The vector is then
rotated accordingly so that the sides are split correctly.
But following the center path would not be very efficient,
so the model predictive [4] motion controller computes its
own optimal local trajectory. The MPC [10] used optimises
the trajectory using the latest perception state and incor-
porates the mechanical constraints of the vehicle’s motion
model, such as maximum linear acceleration and decelera-
tion, actuator delays and disturbances resulting from vehicle
dynamics, e. g . sideslip angle.
V. RESULTS
In order to build a foundation for simulation of the race
car in software that integrates well into ROS, TUW Racing
extended its approach to the Pioneer 3AT and used gazebo
simulations to test marker mapping, odometry corrections
based on simulated IMU data, trajectory planning, as well
as a basic self-developed motion controller. Fig. 5 shows
the robot following a narrow turn in the simulation, with
obstacles of width and height comparable to those of cones
on the track. It successfully navigates a 180deg turn, detects
shapes based on simulated laser scans and determines the
center path accurately.
In Fig. 6 results with a cone detection algorithm on a
still from a video of last year’s autocross race at the FSG Fig. 6. Cone detection on still of last year’s race recorded using a GoPro
camera
are shown. The cones detected in the image are enclosed
by yellow rectangles. The algorithm is based on a trained
cascade classifier using a histogram by an oriented gradients
feature descriptor. Photos of the specific cones seen in the
image, from angles of 0deg horizontal to about 40deg turned
downwards, served as training data. The algorithm turned out
to be computationally expensive and susceptible to variations
in lighting and weather conditions. With the trained algo-
rithm on last year’s video we achieved an unstable detection
rate of 30% to 90% depending on the track section. The
algorithms explained above, evaluated on recordings with
the stereo camera, detected 75% of the traffic cones in
the image regardless of lighting. Most of the missed cones
were affected by very strong shadows cast by large nearby
obstacles, wear of the cone itself or cones that partially
covered each other. The location, color and shape of the
cones, the point of view, background, the vehicle itself and
lighting are very similar to the scene in the image.
Evaluation of detection on a recording of last year’s race
has shownthatdifferent light conditionscanbeaproblemfor
the car’s visual sensor. Extreme light exposure to the camera
or even driving from shade into sunlight can be disturbing,
as the camera needs some time to adjust to the brightness.
Similar issues emerged in evaluation of the stereo camera
mounted on the car driving a short sample track. In Fig. 7,
the camera adjusts to increasing brightness using integrated
exposurecontrolwhen thecarentersa sunlight-drenchedpart
of the track. As the more distant cones become visible by
the aperture’s adjustment, the cone marked with the orange
55
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Title
- Proceedings of the OAGM&ARW Joint Workshop
- Subtitle
- Vision, Automation and Robotics
- Authors
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas Müller
- Bernhard Blaschitz
- Svorad Stolc
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wien
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände