ROBUST METHODS FOR ROBOT LOCALIZATION UNDER

CHANGING ILLUMINATION CONDITIONS

Comparison of Different Filtering Techniques

Lorenzo Fernández Rojo, Luis Payá, Oscar Reinoso, Arturo Gil and Miguel Juliá

Departamento de Ingeniería de Sistemas Industriales, Miguel Hernández University

Avda. de la Universidad s/n. 03202, Elche (Alicante), Spain

Keywords: Omnidirectional vision, Robot mapping, Appearance-based methods, Robust localization and illumination

effects filtering.

Abstract: The use of omnidirectional systems provides us with rich visual information that allows us to create

appearance-based dense maps. This map can be composed of several panoramic images taken from different

positions in the environment. When the map contains only visual information, it will depend heavily on the

conditions of the environment lighting. Therefore we get different visual information depending on the time

of day when the map is created, the state of artificial lighting in the environment, or any other circumstance

that causes a change in the illumination of the scene. To obtain a robust map against changes in the

illumination of the environment we apply different filters on the panoramic images. After that, we use some

compression methods that allow us to reduce the amount of information stored. We have conducted a

comprehensive experimentation to study which type of filter best adapts to changing lighting conditions.

1 INTRODUCTION

When a robot or a team of robots have to carry out a

task in a given environment, in most cases, an

internal representation is required to allow the robot

to estimate its initial position and orientation, and

navigate to the target points. Omnidirectional vision

systems are commonly used at this kind of

applications due to their low cost and the amount of

information they provide. When working in

unstructured environments where the creation of

appropriate models of recognition can be an arduous

chore, it is useful to use appearance-based

approaches that offer a systematic and intuitive way

to construct the map. The main problem such

approaches present is the high computational cost

because they do not extract relevant information

from images, using the image as a whole.

To alleviate the high computational cost, several

researchers have shown how it is possible to use a

representation of the environment in a lower order

subspace, using compression techniques. A widely

extended method is PCA (Principal Components

Analysis). One example is the database created in

(Kröse, Bunschoten, Hagen, Terwijn and Vlassis,

2004). Uenoara and Kanade (1998) studied the

problem of rotation in the plane in which the robot

moves, using a set of rotated images. Jogan and

Leonardis (2000) applied these concepts to an

appearance-based map of an environment. Other

related works (Menegatti, Maeda and Ishiguro,

2004) defined the concept of Fourier Signature and

(Rossi, Ranganathan, Dellaert and Menegatti, 2008)

used the Spherical Fourier Transform of

omnidirectional images, using the Discrete Fourier

Transform to compress the information.

Appearance-based techniques constitute a basis

framework to other robotics applications, as in

route-following, as Payá, Reinoso, Gil and Sogorb

(2008) show.

The appearance of an image will depend, in

general, on the appearance of the objects that appear

on it. Adini, Moses and Ullman (1997) show the

influence of the illumination of the scene in a

process of facial recognition. An individual cannot

be recognized if there is a substantial change of

lighting in the scene. Murase and Nayar (1994) use

an appearance-based approach to avoid the problems

of illumination variation. With this aim many views

of the object are generated under different lighting

conditions. Faraid and Adelson (1999) show that it is

possible to separate the effects of reflections and

223

Fernández Rojo L., Payá L., Reinoso O., Gil A. and Juliá M. (2010).

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS - Comparison of Different Filtering Techniques.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Artiﬁcial Intelligence, pages 223-228

DOI: 10.5220/0002705002230228

Copyright

c

SciTePress

illumination using ICA (Independent Component

Analysis). Other researchers (Bischof, Wildenauer

and Leonardis, 2004) have shown how to mitigate

the effects of lighting on the appearance of an

object, using gradient filter banks. The approach

consists in implementing a series of filters before

building the linear subspace using PCA. Other

works (De Araújo, Maia, D´Angelo and D´Angelo,

2006) make use of homomorfic filters banks to

separate the components of luminance and

reflectance. This way it is possible to filter these

components separately, reducing significantly the

dependence of image appearance with respect to

changes in lighting.

In this paper we present a methodology to build

an appearance-based dense map. Several kinds of

filters and compression techniques have been tested

to make the map robust against changes in lighting

conditions.

The work is structured as follows. Section 2

introduces some filtering techniques to eliminate the

dependence on changes in the lighting. Section 3

presents some compression techniques to reduce the

computational cost. In section 4 the method to build

the map and how to obtain the position of the robot

is detailed. We show the results of experiments

carried out in section 5. Finally, in section 6, we

present the conclusions of the work.

2 FILTERING TECHNIQUES

USING PANORAMIC IMAGES

The appearance of an object in an image can vary

strongly depending on the kind and level of

illumination of the scene. When we work with the

appearance of panoramic images, it is necessary to

take into account the fact that appearance is

influenced both by the position and shape of the

objects and the lighting conditions. It is therefore

necessary to implement a mechanism that allows us

to work independently of the lighting conditions of

the environment.

Several researchers have studied how to get

invariance with respect to the illumination of the

scene in object recognition tasks. We have separated

the different methods in two fields. The first one is

related to the application of a bank of gradient (first

derivative) or Laplacian (second derivative) filters.

The second one consists in performing a

homomorfic filtering of the image separating the

luminance from the reflectance component.

2.1 Edge Detector

The main advantage of using a representation of the

image edges resides mainly in the fact that we obtain

a compact representation and that, in most cases, it is

insensitive to changes in the lighting on the objects

of the image.

An edge detection filtering can be carried out

through the Prewitt gradient filter, based on the

estimation of the modulus of the gradient using two

masks of size 3x3 (h

1

in the x-axis and h

2

in the y-

axis):

h

1

=

−

1

−

1

−

1

000

111

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

h

2

=

−101

−101

−101

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

(1)

An evolution of the Prewitt Filter is the Sobel

filter that, apart from estimating the value of the

modulus of the gradient, produces a smoothing of

the image that may be beneficial, taking into account

the noisy behaviour that the estimations based on the

derivation of the image may present:

h

1

=

−

1

−

2

−

1

000

121

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

h

2

=

−101

−202

−101

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

(2)

Another method for detecting edges is the

Laplacian of Gaussian operator, which combines the

effect of a Gaussian smoothing with the

improvement in the location of the edge (cross of 0

for the second derivative). In this case it is only

necessary to apply a mask:

h

2

=

1

−

21

−24−2

1 −21

⎡

⎣

⎢

⎢

⎢

⎤

⎦

⎥

⎥

⎥

(3)

2.2 Homomorfic Filter

The Homomorfic filter can separate the components

of luminance and reflectance of an image (Gonzalez

and Woods, 1993). Thus it is possible to build a

filter for each component separately, allowing us to

control the contribution of each component on the

image appearance. It is possible to separate the

luminance from the reflectance component by

applying the Neperian logarithm operator on the

image:

f

(

x

,

y

)

=

i(

x

,

y

)

×

r(

x

,

y

)

z(x, y) = ln( f (x, y))

z(x, y) = ln(i(x, y)) + ln(r(x, y))

(4)

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224

Once the components are separated, the 2D

Discrete Fourier Transform is computed. It is at this

point that we can filter the image in the frequency

domain:

ℑ z(x, y)

()

=ℑ ln(i(x, y))

()

+ℑ ln(r(x, y))

()

ℑ z'(x, y)

()

=ℑ z(x, y)

()

⋅Η(u,v)

(5)

It will be necessary to perform the inverse

process to obtain the filtered image in the spatial

domain.

The low frequency components are associated

with the illumination of the image and the high

frequency ones with the reflectance of the image.

So, to reduce the effects of changes in the

illumination of the image, a high pass filter could be

applied. We build this high pass filter from a low

pass one in the next way:

Η'

hp

(u,v) =1−Η

lp

(

u

,v)

Η

hp

(u,v) =

α

h

−

α

l

()

⋅Η'

hp

(u,v) +

α

l

(6)

We have used two families of filters, Butterworth

and Gaussian. Fig. 1 shows the Homomorfic Filter

Transfer Function from a Butterworth filter. The

transfer functions are as follows:

D(u,v) = u

2

+ v

2

()

1/ 2

Η

Butt

(u,v) =

1

1+

D(u,v)

D

0

⎡

⎣

⎢

⎤

⎦

⎥

2n

Η

Gauss

(u,v) = exp −

D(u,v)

D

0

⎡

⎣

⎢

⎤

⎦

⎥

2

⎛

⎝

⎜

⎞

⎠

⎟

(7)

3 COMPRESSION TECHNIQUES

USING PANORAMIC IMAGES

The map created is composed of a set of panoramic

images of the environment. To reduce the

computational cost, it is necessary to extract the

most relevant information from the set of panoramic

images. In this section, some techniques that allow

us to obtain this information are outlined.

Figure 1: Transfer Function of Homomorfic Filter with

Butterworth Filter.

3.1 PCA-based Techniques

As (Kirby, 2000) shows, it is possible to transform

each image from a set of N images with M pixels

each,

N

j

x

M

xj

…

1;

1

=

ℜ

∈

, into a vector with the K

PCA features that contain the most relevant

information,

N

j

p

K

xj

…

1;

1

=

ℜ

∈

,

K

≤

N

. However,

when we build the database in this way, it only

contains information about the direction that the

robot had when each image was captured, but not for

all the possible orientations. Jogan and Leonardis

(2000) present a method to include this orientation

information, with the uniqueness that it is only

necessary to acquire an image per position, and

Payá, Fernández, Reinoso, Gil and Úbeda (2009)

make use of it in a robot localization task,

comparing to other techniques.

In brief, to construct the covariance matrix C, we

obtain Q rotations from each image of the map. As

we work with panoramic images, the covariance

matrix of our data matrix

X ∈ℜ

Mx(QxN )

, shall consist

of a set of N blocks of size Q x Q:

[

]

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

==⇒

=

NNNN

N

N

T

N

XXX

XXX

XXX

XXC

…

…

…

…

21

22221

11211

21

XXXX

(8)

The covariance matrix is composed of circulant

blocks. This fact allows us to perform the SVD

decomposition of C through Q decompositions of

order N, thus reducing the computational cost of the

compression.

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS -

Comparison of Different Filtering Techniques

225

Figure 2: Sets of test images. (a) Test 1 (9:00, artificial light), (b) Test 2 (9:00, artificial light, 90 degrees rotation), (c) Test

3 (18:00, no light), (d) Test 4 (11:00, natural light, 90 degrees rotation), (e) Test 5 (13:00, daylight) and (f) Test 6 (16:00,

daylight).

3.2 Fourier-based Techniques

When we have an image f(x,y) with N

y

rows and N

x

columns, we can obtain the relevant information of

the image by applying the Discrete Fourier

Transform. There are several possibilities, such as to

implement the 2D Discrete Fourier Transform (Payá

et al, 2009), (Rossi et al, 2008), the Spherical

Fourier Transform of omnidirectional images or the

Fourier Signature of the panoramic image

(Menegatti et al, 2004).

The Fourier signature exploits better the

invariance to ground-plane rotations in panoramic

images. This transformation consists in expanding

each row of the panoramic image

{}

{

}

110

,,,

−

=

Nyn

aaaa …

using the Discrete Fourier

Transform into the sequence of complex numbers

{}

{

}

110

,,,

−

=

Nyn

AAAA …

. The most important

information is concentrated in the low frequency

components of each row. It is possible to prove that

if each row of the original image is represented by

the sequence

a

n

{}

and each row of the rotated image

by

a

n−q

{

}

(being q the amount of shift), when the

Fourier Transform of the shifted sequence is

computed, we obtain the same amplitudes

k

A

than

in the non-shifted sequence, and there is only a

phase change, proportional to the amount of shift q

(eq. 9).

ℑ a

n− q

{}

[]

= A

k

e

− j⋅

2

π

qk

N

y

; k = 0, ..., N

y

−1

(9)

4 MAP BUILDING AND

LOCALIZATION

In this section, we expose in general terms, how a

dense map can be built, and how the location and

orientation of the robot in it can be computed. (Payá

et al, 2009) evidence that use of the Fourier

signature of the image clearly outperforms PCA both

in time consumption and in localization accuracy.

Therefore to create the map and retrieval of the

location, we use only the Fourier Signature of the

image.

4.1 Map Building

To perform the experiment, we have captured a set

of 101 omnidirectional images on a predefined grid

of 40x40 cm in an indoor environment. We work

with panoramic images with a size of 56x256 pixels.

Once we have all the panoramic images, we used the

Fourier signature described in the previous section.

To test the validity of the maps constructed, we have

captured several test images in some half-way points

among those stored in the map. We have captured

several sets of test images with changing

illumination conditions and changing the position of

some objects (Fig. 2). Fig. 3 (a) shows a bird’s eye

view of the grid used to capture the images to

construct the map and an example of panoramic

images.

4.2 Localization and Orientation

Recovering

The objective is to calculate the position and

orientation of the robot in the points where the test

images where taken, under different lighting

conditions, using only the visual information stored

in the map.

To calculate the position and orientation of the

robot for each test image, we calculate the Fourier

transform (using the Fourier signature) and then, we

calculate the Euclidean distance of the power

spectrum of the test image with respect to the spectra

stored in the map. The corresponding position of the

robot is extracted as the best matching. Furthermore,

the orientation is calculated with eq. 9.

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

226

Figure 3: (a) Grid used to capture the set of training images, (b) Precision versus Recall localization without filtering, (c)

Precision versus Recall localization using Gradient Filters, (d) Precision in terms of frequency of the filters, (e) Precision in

terms of maximum and minimum value of the filter, (f) Precision versus Recall localization using Homomorfic Filters.

5 EXPERIMENTAL RESULTS

In this section the results obtained from experiments

are presented. To perform the experiments, we have

constructed the map by taking a total of 101

panoramic images. We have 6 sets with 17 test

images each, taken at different times of day and

under different conditions of illumination.

We will use Recall and Precision charts (Gil,

Martinez, Ballesta and Reinoso, 2009) to compare

the different methods of filtering. The parameters

are defined as follows:

matchescorrecttotal

retrievedmatchescorrect

recall

#

#

=

retrievedmatches

retrievedmatchescorrect

precission

#

#

=

(10)

For the data association we use the minimum

Euclidean distance, through the descriptors Nearest

Neighbour (N.N.), Second Nearest Neighbour

(S.N.N.) and Third Nearest Neighbour (T.N.N.). Fig.

3 (b) shows the results obtained when we perform a

localization process without prior filtering. We can

observe the improvement that occurs when we use

the descriptor T.N.N.

In Fig. 3 (c) we can observe how worse results

are obtained when we apply gradient-based filters. It

reduces the accuracy from 46.08% (no filter) to

41.18% (Sobel), 37.25% (Prewitt), or, in the worst

case to 36.27% (Laplacian).

When working with homomorfic filters the

parameters of the filter need to be adjusted

previously. The homomorfic filter built using a

Butterworth filter depends mainly on the cut-off

frequency, the order of the filter and the maximum

and minimum value of the filter. When we build the

filter from a Gaussian filter, the most important

parameters are the cut-off frequency and maximum

and minimum value of the filter. As we can see in

Fig. 3 (d) and Fig. 3 (e), both filters are more

dependent on the maximum and minimum values,

that the cut-off frequency.

After exhaustive tests, the optimal values for the

parameters are a cut-off frequency of 50 Hz,

Butterworth filter of order 3, homomorfic filter

maximum value equal to 0.21 and minimum value

equal to 0.20. Fig. 3 (f) shows how we can improve

the accuracy of the location within the map,

applying a homomorfic filter to it. In this case we

have passed from an accuracy of 46.08% (no filter)

to an accuracy of 60.78% with the homomorfic

ROBUST METHODS FOR ROBOT LOCALIZATION UNDER CHANGING ILLUMINATION CONDITIONS -

Comparison of Different Filtering Techniques

227

filter. We can see how the results obtained with the

Gaussian filter are almost identical to those obtained

using the Butterworth filter.

6 CONCLUSIONS

In this work, we have presented some methods for

the creation of robust dense maps of real

environments, using an appearance-based approach

from previously filtered panoramic images.

We have presented two possible methods for

filtering against illumination changes in the

environment. As shown, the application of the first

method (edge detection), not only does not improve

but also worsens the results. On the other hand,

applying a homomorfic filter on the panoramic

image significantly improves the localization. Very

similar results are obtained when constructing the

homomorfic filter using a Gaussian filter or using a

Butterworth filter. Furthermore, we have tuned the

parameters of the filters to obtain a robust location

against changes in illumination.

We have built the database by applying a

compression of the visual information. We have

used the Fourier signature due to the fact that it

presents better results in terms of amount of memory

and computation times needed to build the database.

It is also important the fact that it presents

orientation invariance and it allow us to compute the

robot orientation. Finally, an important property is

that the Fourier transform is an inherently

incremental method. These properties make it

possible to be applied in future works where robots

have to add new information to the map and localize

simultaneously in real time.

This work opens the door to the use of

appearance-based methods with applications in

mobile robots. As we have shown, the map created

is robust against changes of lighting conditions, and

it permits thus to recover the location and orientation

of the robot in the map even if there are changes in

the illumination of the scene.

ACKNOWLEDGEMENTS

This work has been supported by the Spanish

government through the project DPI2007-61197.

‘Sistemas de percepción visual móvil y cooperativo

como soporte para la realización de tareas con redes

de robots’.

REFERENCES

Adini, Y., Moses, I., Ullman, S., 1997. Face recognition:

the problem of compensating for changes in

illumination direction. In IEEE Trans.Pattern Analysis

and Machine Intelligence, Vol. 19, No. 7, pp. 721-732.

Bischof, H., Wildenauer, H., Leonardis, A., 2004.

Illumination intensitive recognition using eigenspaces.

In ELSEVIER Computer Vision and Image

Understanding, Vol. 95, No. 1, pp. 86-104.

De Araujo, V., Maia, R., D’Angelo, M., D’Angelo, G.,

2006. Automatic Plate Detection using genetic

algorithm. In Proc. World Scientific and Engineering

Academy and Society, pp. 43-48.

Faraid, H., Adelson, E., 1999. Separating reflections and

lighting using independent components análisis. In

Proc. IEEE Computer Society Conf. Computer Vision

and Pattern Recognition. Vol. 1, pp. 1262-1267.

Gil, A., Martinez, O., Ballesta, M., Reinoso, O., 2009. A

comparative evaluation of interest point detectors and

local descriptors for visual SLAM. In SPRINGER

Machine Vision and Applications.

Gonzalez, R.C., Woods, R.E., 1993. Digital Image

Processing. Ed. Addison Wesley.

Jogan, M., Leonardis, A., 2000. Robust Localization

Using Eigenspace of Spinning-Images. In Proc. IEEE

Workshop on Omnidirectional Vision, Hilton Head

Island, USA, pp. 37-44, IEEE.

Kröse, B., Bunschoten, R., Hagen, S., Terwijn, B.

Vlassis, N., 2004. Household robots: Look and learn.

In IEEE Robotics & Automation magazine. Vol. 11,

No. 4, pp. 45-52.

Menegatti, E.; Maeda, T. Ishiguro, H., 2004. Image-based

memory for robot navigation using properties of

omnidirectional images. In Robotics and Autonomous

Systems. Vol. 47, No. 4, pp. 251-276.

Murase, H., Nayar, S., 1994. Illumination planning for

object recognition using parametric eigenspaces. In

IEEE Trans. Pattern Anal. Mach. Intell., Vol 16, pp.

1219-1227.

Payá, L., Reinoso, O., Gil, A., Sogorb. J., 2008. Multi-

robot route following using omnidirectional vision and

appearance-based representation of the environment.

In Lecture Notes in Artificial Intelligence. Vol. 5271,

pp. 680-687 Springer.

Payá, L., Fernández, L., Reinoso, O., Gil, O., Úbeda, D.,

2009. Appearance-based dense maps creation.

Comparison of compression techniques with

panoramic images. In Int. Conf. on Informatic in

Control, Automation and Robotic, Vol. 6, pp. 238-246.

Rossi, F., Ranganathan, A., Dellaert, F., Menegatti, E.,

2008. Toward topological localization with spherical

Fourier transform and uncalibrated camera. In Proc.

Int. Conf. on Simulation, Modeling and Programming

for Autonomous Robots. Venice (Italy), pp. 319-330.

Ueonara, M., Kanade, T, 1998. Optimal approximation of

uniformly rotated images: relationship between

Karhunen-Loeve expansion and Discrete Cosine

Transform. In IEEE Trans. Image Processing. Vol. 7,

No. 1, pp. 116-119.

ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence

228