Obstacle Detection Using Adaptive Color Segmentation

Obstacle Detection Using Adaptive Color Segmentation
Obstacle Detection Using Adaptive Color Segmentation and Color Stereo
Homography
Parag H. Batavia and Sanjiv Singh
[parag/ssingh]@ri.cmu.edu
Carnegie Mellon University
Robotics Institute
Pittsburgh, PA 15213
Abstract
Obstacle detection is a key component of autonomous systems.
In particular, when dealing with large robots in unstructured
environments, robust obstacle detection is vital. In this paper,
we describe an obstacle detection methodology which combines two complimentary methods: adaptive color segmentation, and stereo-based color homography. This algorithm is
particularly suited for environments in which the terrain is relatively flat and of roughly the same color. We will show results
in applying this method to an autonomous outdoor robot.
1. Introduction
This paper describes an obstacle detection algorithm for
use in relatively flat areas where there is similarity in
color. The method is robust to false positives and negatives through the use of two complimentary methods:
color segmentation and color homography.
Color segmentation, as the name implies, uses color to
classify image areas as “obstacle” or “freespace” The
method we use is based on a training algorithm, in
which examples of “freespace” are shown to the system,
and it learns appropriate representations.
Stereo-based homography is often referred to as “poor
man’s stereo”. Although computationally cheap, it does
not provide depth information, as pure stereo does.
Rather, it provides information on whether a particular
image feature rises above the ground plane. In applications where a complete depth map is not needed, this
can be a computationally cheap alternative. We extend
the homography formulation to make use of color information, which improves robustness. This system is used
to automatically train the color segmentation system.
In the rest of this paper, we describe how both methods
are combined to form a robust obstacle detection system, followed by an example of its use on an outdoor
mobile robot.
2. Obstacle Detection
Obstacle detection is a key component of an autonomous robot, particularly when dealing with large outdoor vehicles. The robot has to have very robust obstacle
detection capabilities, since it is a heavy, potentially
dangerous piece of equipment. The size of obstacles can
vary, and the detection system has to operate reliably in
various lighting conditions, along with light fog and
rain, and at night as well. We have examined two methods for detecting obstacles, along with methods for integration of these two methods.
2.1. Color Segmentation
The basic idea behind color segmentation for obstacle
detection is that pixels in an image are classified as
“obstacle” or “freespace” based on color. When operating in domains in which traversable areas are of relatively constant color, such as grass, color segmentation
works well.
Each pixel in a color image consists of a 3-tuple, representing the amount of energy contained in the red,
green, and blue bands. Typically, each component of the
tuple is a value between 0 and 255. Therefore, one simplistic method of color segmentation is a rule-based system. In such a system, various rules would be used to
classify pixels, such as “if red is between 100 and 175
and green is less than 25 and blue is more than 70, then
classify as freespace”.
While conceptually simple and extremely fast, such
methods are not general. They are specific to lighting
conditions, and camera performance, and can easily be
fooled by shadows and variations in grass condition.
Therefore, we use a general, non-parametric representation similar to that used by Ollis [6] and Ulrich [12], but
with extensions for automated training and adaptation.
This approach uses a probabilistic formulation to classify pixels, based on a set of training images.
These images are not stored as standard (R,G,B) tuples.
Rather, they are first converted to a different color space,
known as Hue-Saturation-Value, or HSV. This is a cylindrical space, in which the H and S components contain
the color information, in the form of a standard color
wheel. The Hue is the actual color, or the angle of the
point in the cylinder, the Saturation is the “purity” of the
color, and is the radial distance of the point. The Value is
the intensity, or brightness, and is the height of the point.
This space has the advantage that if we ignore the Value
component, we get additional robustness to shadows and
illumination changes, along with a reduction in feature
dimensionality.
Other researchers have addressed the problem of color
segmentation. Hyams [3] uses a Spherical Coordinate
Transform, which is a color space previously used in the
medical domain, combined with a nearest-neighbor segmentation scheme to localize daughter vehicles with
respect to a mothership. Shiji [9] uses a watershed algorithm along with extensions to avoid over-segmentation.
McKenna [5] uses an adaptive mixture model to represent classes, which is a more compact representation
than ours, but is computationally more expensive to
train. The dichromatic reflection model, originally proposed by Shafer [8], is still used as well, as seen in [4].
Training Images
Training Histogram
Test Image
Testing Histogram
2.1.1. Training
The training set is represented as a two dimensional histogram. The bins in the histogram are addressed based
on the H and S values of a color pixel. The contents of
the bin denote the number of occurrences of that particular H and S pair in the training set.
The color segmentation system has to “learn” what colors constitute traversable areas, such as grass. To train
the classifier, we present it with several images of grass
taken in various lighting conditions. For each pixel in
the training image, the value of the corresponding histogram bin is incremented. Therefore, colors that occur
often will have high values in the histogram. After training is done, the histogram is normalized by the total
number of samples (i.e., the sum of the bin contents), so
that bin contents now represent a probability. The top
third of Figure 1 illustrates the training procedure.
The top-left image shows a set of training images of
grass. The color values contained in these images are
added to the histogram, which is shown in the top-right
image. The histogram is represented as a color wheel.
The area to the right of the center of the wheel, which
corresponds to various shades of green, shows activity,
which represents the colors in the training set.
The training time is linear in the number of image pixels, and in practice is extremely fast. The training can be
done in a supervised manner by showing examples of
freespace. Alternatively, the training set can be automatically acquired and adapted using homography, a complementary method
2.1.2. Run-Time
After training, the system is ready to classify pixels as
obstacle or freespace. For each pixel, p, in a test image,
we look up the bin value corresponding to the color of p.
This provides us with a probabilistic measure, P, of p
Obstacle Image
Figure 1: Color Segmentation training and histogram details.
being in the training set. If P is greater than a threshold,
then we classify it as freespace. Else, it is an obstacle.
I.e, anything which is not freespace is classified as an
obstacle. This can lead to false positives, as we will discuss later.
The middle third of Figure 1 shows a test image, which
contains grass and a bag. The figure on the middle-right
shows the color distribution of the test image, again represented as a color wheel. Notice that although a large
portion of the test image color distribution overlaps the
training set color distribution, there is a significant portion which does not. This portion is due to the presence
of the bag. The lower right figure shows an “obstacle
image,” in which white indicates obstacle and black
indicates freespace. The bag is accurately detected, and
there are no false positives.
2.1.3. Performance
Color segmentation relies on having a complete training
set. As lighting changes, due to time of day or weather
conditions, the appearance of grass and obstacle change
as well, since the amount of incident sunlight changes.
The color of grass is different under a cloud cover than
under direct sunlight, and is different in the morning vs.
mid-day. This can lead to false positives, if the system is
only trained in one lighting condition, and then is used
in another. Since training is so fast, and can be done on
the fly, this is not a severe issue. If the environmental
lighting changes, we can simply re-train and continue.
Similarly, color segmentation can classify flat objects,
such as fall leaves, as obstacles, since their color is different from grass. In these cases, it is safe to drive over
them. Therefore, there are four possible cases: 1) No
obstacle, 2) true obstacle with significant height, 3) true
flat obstacle, and 4) false obstacle due to lighting
change. In cases 1 and 2, we do not need to modify the
training set. In case three, we do not want to modify the
training set, but want to recognize that it is safe to proceed. In case 4, we need to augment our training set to
handle the new environmental conditions.
The next section describes stereo homography, which is
a computationally cheap yet powerful method for
detecting objects which rise above the ground plane.
Homography can provide enough information to disambiguate between cases 2 and 3 or 4.
2.2. Color Homography
In conventional stereo, multiple cameras are used to find
the range to image features. This range information
comes at a steep computational cost. Computing depth
using stereo is of the order O(m*n*d), where m is the
number of image pixels, n is the size of the correlation
window, and d is the number of disparities searched,
which is related to the range of depths which can be
found. Another way to find obstacles is to use homography, which is linear in the number of image pixels. This
is because homography does not compute range. Rather,
it provides just enough information to determine
whether a particular image feature is on the ground or
above it.
The basic idea behind homography is this: If we know
the extrinsic and intrinsic parameters of both cameras,
and assume that all image features lie on the ground
plane, we can solve the inverse perspective problem.
I.e., any given image point in the [left/right] camera can
now be back-projected into world coordinates. These
world coordinates can then be forward-projected into
the opposite camera. Using the left camera as an example, we can warp the left camera image to the right camera, and then compare the warped image against what
the right camera actually sees. If all the image features
actually do lie on the ground plane, then the warped
image will match the actual right camera image. However, if certain image features lie above the ground
plane, then our warped image will be incorrect in those
areas, and this discrepancy can be detected.
Figure 2: Homography calibration images.
Previous work, as described in the next section, has
made use of grey scale intensity images. Often, objects
of different colors have the same intensity as the ground
plane. In these cases, detecting ground plane violations
through image subtraction fails. To avoid this, we use
hue images, which capture the color properties of the
background and potential obstacles.
2.2.1. Image Warping
We do a perspective warping of the left image to the
right image, based on a 3x3 homography matrix. The
equation to do the warping is:
x' = Hx
(1)
Where x’ is the (u,v,1) homogenous coordinate of the
left image, x, and H is the homography matrix.
H is determined through a calibration procedure,
described fully in [11], in which an image pair is taken,
and a small set (usually four) of corresponding features
in the left and right image are manually selected. These
features must lie on the ground plane. A sample pair of
calibration images is shown in Figure 2. Typical features
to mark would be the corners of the white calibration
markings.
Four corresponding features provides enough constraints to solve for the 8 free parameters in H (the 3,3
element of H is always 1). However, in practice, this
yields a sub-optimal solution. Therefore, a levenbergmarquardt non-linear optimization step is applied to find
the optimal value for H.
Once calibration is accomplished and H is found, obstacles can be detected. Figure 3 shows an example of
homography being used for obstacle detection.
The top-left and top-right images are the left and right
camera input images, respectively. The bottom-left
image is the left image, warped as it would be seen from
the right camera, given the assumption that all features
lie on the ground. Note that there is a triangular black
area in the warped image, on the right side. This is due
to a lack of information, since the left camera field of
view does not extend as far right as the right camera
field of view. The bottom-right image is a thresholded
2.3. Integration
Given two working, complimentary, obstacle detection
methods, integration is an issue. Both methods have different strengths and weaknesses, and it is important to
integrate them in such a way that the strengths of each
are used to offset the respective weaknesses. For
instance, color segmentation, although able to detect
small obstacles and changes in color, is not sensitive to
obstacle geometry, such as height. Homography is only
able to find obstacles that are over a certain height. Both
methods produce a list of candidate obstacles and centroid locations. It would be possible to just combine
them in an ‘OR’ fashion. Alternatively, they could be
‘AND’ed, so that both methods would have to detect a
particular obstacle.
Figure 3: Homography example. The top images are input
images. The bottom left image is a warped image. The bottom
right image is the obstacle image.
difference image between the right camera image and
the warped image. The two white areas in the difference
image correspond to the portions of the obstacle which
did not match the prediction, since it lies above the
ground plane.
Previous work includes work by Storjohann [10], for
indoor applications of homography and inverse perspective mapping. Batavia [1] has used a monocular version
of homography, utilizing a single camera and known
ego-motion, rather than two cameras, for highway
obstacle detection. Santos-Victor [7] also used a monocular approach, but with a formulation that did not
require knowledge of ego-motion. However, this
approach requires the computation of normal-flow vectors. Bertozzi [2] used a stereo approach for detecting
highway obstacles, with an on-line calibration-tuning
ability.
2.2.2. Performance
This method is robust to the types of false positives
which confound color segmentation. The sensitivity to
true obstacles is determined by the image resolution,
calibration accuracy, and field of view. This can be
improved by increasing the image resolution and/or narrowing the field of view. In general, the same issues
which affect stereo accuracy have an impact on homography accuracy.
Another issue is sensitivity to pitch variations. This variation can come from platform vibration, or it can come
from a change in the terrain slope, which breaks the
ground plane assumption. Large deviations in pitch from
the calibrated conditions can lead to false positives, as
the image warping process is dependant on a pre-determined pose.
We use homography to act as a ‘false positive’ filter for
color segmentation. When color segmentation detects an
obstacle, homography is used to decide whether the
obstacle is rising above the ground or not. If it is, then
the object is classified as an obstacle. If it is not, then a
decision has to be made whether to adaptively re-train
the color segmentation system (in the case of global
lighting change) or whether to simply ignore the object,
assuming it is a temporary obstacle (such as a leaf). This
decision is made based on the size of the obstacle. If it is
extremely large, subtending most of the image, then it is
likely that there is no object at all, and it is a global
lighting change, and we re-train. Using homography as a
filter allows us to adaptively re-train on the fly, without
operator intervention.
3. Experimental Results
We have tested the fully integrated obstacle detection
system offline, on image sequences, and online, using a
mobile robot. We have also done extended duration testing using only color segmentation. The platform we use
for online testing is described in the next section
3.1. Platform Description
The platform used for these experiments is a riding
lawnmower and is pictured in Figure 4. The front
bumper contains two CCD cameras and a SICK laser
scanner.
Currently, an integrated PC, mounted in the rear, is used
to communicate with the sensors and control the vehicle. The steering and throttle are hydraulically controlled, and are both actuated. A serial protocol is used
to set steering and throttle positions. The planner and
trajectory generation module takes pose information as
input, and generates trajectory commands as output, and
False Obstacle Difference Image Histogram
3000
Figure 4: Riding lawnmower with cameras and laser range
finder.
passes them to the command and safety arbiter, which
executes commands, based on input from the obstacle
detection subsystem.
A dead-reckoning system is used for navigation, which
integrates odometry information from two wheelmounted encoders, along with heading information from
a fiber optic rate gyro. Dead-reckoning will accumulate
error over time. Over large distances, some form of globally-referenced localization will be required to bound
the dead-reckoning error.
3.2. Results
The first test involves offline processing of two image
sequences. The first sequence contains one true obstacle
-- a small red fire extinguisher, and is the same sequence
of Figure 3. The second sequence contains a false positive -- a large area of discolored grass and sand.
Figure 5 shows results from the two sequences. The top
two images are the left and right camera images of the
sand sequence. The middle left image is the color segmentation output on the left input image. The middle
right image is a histogram of the homography difference
image. Recall, the difference image is an absolute difference between the hue of the actual right input and the
hue of the warped image. The bin centers are difference
values, and the counts indicate the number of pixels in
the difference image which fell into the corresponding
bin. Peaks at high difference values indicate the presence of an obstacle. The middle right figure shows only
a peak at very low difference values, indicating that
homography does not detect an obstacle. Therefore, the
output of the color segmentation is actually a false positive.
In contrast, the bottom two figures show an input image
from the fire extinguisher sequence, and the corresponding difference histogram. Note the peak at higher difference values. This indicates the presence of an obstacle.
The output of the color segmentation is true in this case.
0
0
Difference Value
90
True Obstacle Difference Image Histogram
3000
0
0
Difference Value
90
Figure 5: Offline homography results on true obstacle vs.
false positive.
Our online testing made use of the lawnmower platform.
The robot navigated autonomously through an oval pattern, travelling a total of 200m over 7 minutes. The color
segmentation system starts with an initial training set of
one image of grass. During this period, a “false obstacle,” in the form of a green sheet, was set in front of the
path. Figure 6 shows this. This sheet is meant to represent an area of grass which is of different color than the
grass initially used for training. If operating alone, a
human operator would have to decide whether or not to
augment the training set with the new grass, since it
appears as an obstacle.
Instead, color homography is used to validate the color
segmentation output. The sheet is flat on the ground, so
the homography prediction matches what is actually
observed. Therefore, the object is declared a false positive. The decision to add this image to the color segmentation training set is made because the object is larger
than a thresholded size. If the object had been smaller, it
would have been ignored, and the mower would have
continued.
3.3. Extended Duration Results
We conducted another test during a recent demonstration of the color segmentation system. In this test, only
color segmentation was used. The mower runs in
straight swaths of about 10 meters, then turns 180
along with accurate localization, the inverse perspective
equations are solvable for arbitrary terrain. Also, the
color segmentation can be improved through additional
color-constancy work. In particular, accounting for the
spectral contribution of varying sunlight should greatly
reduce the number of false positives.
References
Figure 6: A green sheet simulating an area of new grass. The
lower left image is a depiction of the training set before the
new grass was added. The lower right is the training set after
it was added.
degrees and repeats the pattern. The total distance travelled in this case is about 60 meters, and the total area
mowed is about 80 square meters. Over a recent four
day period, we repeated this pattern approximately 50
times, resulting in a total distanced travelled of about 3
km., and a total area covered of about 4 square km. Over
these 50 trials, false positives were encountered on average of once every 250m of travel. True obstacles were
also placed in its path, with 100% detection.
4. Conclusion and Future Work
We have demonstrated a novel integration of two visionbased obstacle detection methodologies: color segmentation, and color homography. Each method has
strengths which compensate for the other’s weaknesses,
resulting in a robust method for obstacle detection. Furthermore, the homography is used to autonomously train
the color segmentation system, allowing unsupervised
training.
Future work includes further improving the robustness
of the obstacle detection system and navigation system
to allow for unattended operation over larger areas, on
the order of 10 to 20 square kilometers. In particular, the
homography approach is not limited to flat ground.
Given terrain information, in the form of a digital map,
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Acknowledgements
The authors would like to acknowledge the support of Iwan
Ulrich, whose work provided the foundation for the color segmentation-based obstacle detection system described here,
along with lower level camera driver support.
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