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Vertical Line-based Visual Odometry in
Urban area
Ji Zhang and Dezhen Song
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Introduction |
Vertical lines are easy to be
found
in urban area, and easy to be extracted
from images. They are insensitive to
shadow and lighting conditions. In this project, our
idea is to use the vertical lines
such as buildings' edges and poles
to estimation the robot movement.
Since these vertical lines are
sensitive to the robot horizontal
movements, they become excellent
landmarks providing accurate
estimations of the robot ego motion
on the road plane.
In the odometry process, each pair of
vertical lines provides an estimation
result. Because of the different
locations of the vertical line
pairs, they introduce different
amount of errors to the estimation
results. We model the error
propagate process and formulate the
landmark (vertical line pair)
selection process and error variance
minimization process. We
develop real time algorithms that assign each vertical line pair with
a weight according to their
locations in the camera coordinate.
We investigating how different
weighting mechanisms impact the
accuracy of ego-motion estimation.
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Experiments
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We use a Sonly DSC-F828 camera
mounted on a robot in the
experiment. The camera horizontal
field of view is set
to be 50 degrees and the resolution
of the camera is set to
be 640*480 pixels. The robot is
equipped with a MocroStrain 3MD-GX1
inertial measurement unit (IMU) which provides
angular 3D orientation readings. The
robot movement is controlled by a
laptop computer via local wireless
network.
The experiment site is in
front of the Evans library on Texas
A&M University campus where there
are
plenty of vertical edges with a
flat ground. The robot trajectory is
set to be a zigzagging poly line with
each odd
step moving toward the depth
direction and each even step moving
toward the left side as shown in the
above figures. The trajectory includes 31
step with the first step given as a
reference and the following 30 steps
to be estimated.
Our latest vision odometry results [1]
(blue trajectory in the above
figure) are rather accurate (about 2% relative error)
if
comparing to the robot real
trajectory indicated by the black
curve. At the same time, using only a single
vertical line pair, which is the
earlier method we proposed in [2],
gives a slightly little worse result
as illustrated by the
red curve. If we uses
the naive method that equally weights each
vertical line pair, the results are
much worse than the first two as
illustrated by the green curve.
Obviously, the experiment results show that
using the optimized weights yields
the most accurate robot movement estimation.
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Papers |
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Ji Zhang and Dezhen Song, Error Aware
Monocular Visual Odometry using Vertical Line Pairs for Small Robots in
Urban Areas, Special Track on Physically Grounded AI (PGAI), the
Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10),
Atlanta, Georgia, USA, July 11-15, 2010 [pdf
350k][Video
Demo in wmv format 8.7Mb]
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Ji Zhang and Dezhen
Song, On the Error Analysis of
Vertical Line Pair-based Monocular
Visual Odometry in Urban Area, The
2009 IEEE/RSJ International Conference
on Intelligent Robots and Systems
(IROS), St. Louis, USA, Oct. 11-15, 2009
[pdf
750k]
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