CONTINUOUS NAVIGATION OF A MOBILE ROBOT WITH AN

APPEARANCE-BASED APPROACH

Luis Payá, M. Asunción Vicente, Laura Navarro, Oscar Reinoso, César Fernández, Arturo Gil

Departamento de Ingeniería de Sistemas Industriales, Miguel Hernández University, Av. de la Universidad s/n,

Ed. Torreblanca, 03202, Elche (Alicante), Spain

Keywords: Automated learning, Continuous navigation, Appearance-based method, View-sequence route-

representation.

Abstract: Appearance-based approaches have become a feasible technique applied to robot navigation. They are based

on the direct comparison of images without any feature extraction. This approach presents several

advantages comparing to model-based methods, such as their application to non-structured environments

and the relative simplicity of the control they offer. This work presents the continuous navigation of a

mobile robot, using an appearance-based method. The objective is the following of pre-recorded routes,

using just visual information acquired with a couple of parallel cameras. In this approach, low-resolution

frontal images along the route to follow are stored. This is done in an automated way, what allows

optimizing the database size. Several control schemas have been tested to improve the accuracy in the

navigation, such as P, PD and PD with variable parameters, whose experimental results are presented.

1 INTRODUCTION

Conventional research on mobile robots has focused

on approaches that use geometric models to

outperform auto-location and navigation (Lebegue,

1993), (Swain 1999). These techniques make use of

landmarks from the scene as references to guide the

robot through the desired route. The recognition of

patterns is achieved comparing features of the input

image with previously stored features. It supposes

high complexity due to the difficulty in features

extraction and comparison of patterns in realistic and

changing environments.

In appearance-based approaches, images are

memorized without any feature extraction, and

recognition is achieved based on the matching of the

images. It is expected to be useful for complicated

scenes in the real world in which appropriate models

for recognition are hard to create. This approach

consists on two phases; in the learning one, the robot

stores general visual information from several points

of view in the environment, and in the autonomous

navigation, a control action is calculated comparing

the current visual information with the stored one.

These techniques require huge amounts of

memory and high computational cost to store the

necessary information of the environment and make

the comparisons, so researchers have proposed

several methods to outperform auto-location and

navigation trying to minimize the database and the

computing time. Matsumoto et al. addressed the

VSRR (View Sequence Route Representation)

method (Matsumoto, 1996), consisting on the direct

comparison of low-resolution images. Jones et al.

proposed a method using a couple of cameras and

odometer information to carry out navigation (Jones,

1997). The computing time can be reduced working

with the colour histogram instead of the entire image

(Zhou, 2003). Also the complexity of the problem

can be reduced working in the PCA subspace

(Maeda, 1997).

The method proposed is based on the VSRR

model with a couple of cameras, in which the size of

the images to store is reduced by taking a low

resolution, and the size of the database is optimized

using an automated learning phase.

2 CONTINUOUS NAVIGATION

USING LOW-RESOLUTION

IMAGES

The application has been tested over the B21r

mobile robot, which has 4-wheel drive with

synchronous drive kinematics. The driving and the

443

Payá L., Asunción Vicente M., Navarro L., Reinoso O., Fernández C. and Gil A. (2005).

CONTINUOUS NAVIGATION OF A MOBILE ROBOT WITH AN APPEARANCE-BASED APPROACH.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 443-446

DOI: 10.5220/0001184204430446

 SciTePress

steering systems can be controlled independently.

The images are acquired using a couple of Sony

XC999 cameras with their optical axis aligned. The

simultaneous use of two cameras will make our

method more robust.

Previous experiments with 32x32, 64x64 and

128x128 resolutions showed that 32x32 is a good

value due to the low computational cost it supposes,

although it slightly increases the error in the

following (Payá, 2005).

The purpose of the work is to follow pre-

recorded continuous routes. To achieve this, two

phases need to be implemented: a learning stage, in

which some visual information along the route is

stored, and an autonomous navigation phase, in

which the robot estimates its current position and

drives to tend to the learned route.

2.1 Learning phase

In our previous work (Payá, 2005), the route was

decomposed in straight segments before carrying out

the learning phase. Then, the robot was manually

guided through the route to learn, taking images

along the decomposed route in the points the

operator decided. This fact made necessary to store,

apart from the images, the qualitative control action

(left or right) that the robot should execute in the

intersection of two adjacent segments. This way, in

the navigation phase, when the robot arrived to one

of these intersections, it had to stop and begin a pure

turning movement. As well, the separation between

two samples (and so, the size of the database) was

decided by the operator.

The model presented proposes a new learning

method that makes possible continuous navigation,

with no need of storing additional information apart

from the images along the route. Besides, to

optimize the size of the database, the learning phase

has been automated. This means the robot takes

images simultaneously with both cameras at the first

point of the route, and compares continuously the

current views with those previously stored. The

criterion used is the zero-mean cross-correlation.

When the correlation of the current images respect

to the previous stored goes down a threshold, a new

pair of images are acquired and stored in the

database. Fig. 1 shows a possible route with the

points where images have been taken. In the straight

zones, the views change slowly, so new images are

stored with less frequency. When the robot is

turning, the information changes quicker, so the

images are stored more frequently.

2.2 Autonomous navigation

During the autonomous navigation, the robot is

located in a point near the learned route. Then, it has

to recognize which of the stored positions is the

nearest to the current one and drive to tend to the

route, following it till the end. Two processes that

are executed successively have been implemented:

auto-location and autonomous navigation.

Auto-Location: To carry out auto-location, the

current entire images are compared using the zero-

mean cross-correlation with all those previously

stored in the database (Payá, 2005).

During the navigation, the current image must be

compared only with the previously matched and the

following one, because navigation is continuous.

This implies that, once the robot has started

navigation, the time of processing is independent of

the database size, and so, of the length of the route to

be followed.

Control: The robot steering has to be corrected to

make it tend to the route and follow it to the end. It

is achieved through the tracking of two sub-windows

taken on the matched images over the current

images, as shown on fig. 2. The offsets x

and x

allow calculating the necessary steering velocity.

The linear velocity will be proportional to the

average correlation, what means that when the robot

is far from the route, the linear velocity is low to

allow correcting the trajectory, but when the route is

being followed quite well, the robot goes quicker.

This equals to a proportional controller (eq. 1).

(1)

Being k

, k

and k

three constants. Taking into

account the results experimentally obtained in our

previous works (Payá, 2005), the given value to the

constants is: k

= k

= 0.04 and k

= 0.6. γ

is the

arithmetic average of the correlations of the left and

right images. Fig. 3 shows the typical evolution of

the correlation during autonomous navigation.

xkxk

⋅=

⋅+⋅=

ure 1: Database created durin

learnin

sta

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444

The vertical lines indicate the points where the

matching images change. When this occurs,

correlation begins increasing, reaches a maximum

when he passes through the point where images

were stored and begins decreasing until the next

images are matched. Then, this behaviour is

repeated. The average value of this correlation along

the navigation can be used as a measure of the

accuracy in the route following respect the pre-

recorded one. After several experiments with

different values for the learning threshold, the results

obtained are shown on fig. 4.

To improve the behaviour during navigation,

trying to perform it with a better degree of

correlation, several control schemas have been

tested. The second control schema includes

differential effects in the control of linear and

angular speeds.

(2)

The effect this controller has in the navigation is

a foresight of what is going to happen. In the case of

the linear velocity, when the correlation is

increasing, the robot is approaching to the route

correctly. In this case, the derivative factor is

positive, what means that the robot goes quicker

because it is tending to the route correctly. When

correlation decreases, the derivative factor is

negative, so the robot reduces its velocity because it

is moving away the route. This foresight effect can

be applied to the turning speed too. This means that

the differential effect may improve the overall speed

during the navigation and the overall error in the

following of the learned route. This second effect

can be appreciated on fig. 5.

The last control schema tested is based in the

differential one, but making the parameters variable.

(3)

In this control schema, the effect of the

horizontal offset of the left sub-windows is

[

]

(

)

(

)

[

]

()

−

−−

−⋅+⋅=

−+−⋅++⋅=

avD

kkv

xxxxkxxk

γγγ

Figure 2: Tasks performed during autonomous navigation. First one, the robot makes auto-location, comparing

current images with the previously matched, and the next ones. Once we have a match, we calculate, the linear

and steering speeds based o the global correlation and the horizontal displacement of a template

[

]

()()

[]

()

−

−−

−⋅+⋅=

−⋅+−⋅⋅+

+⋅+⋅⋅=

avD

kkv

xxxxk

xxk

γγγ

γγ

γγω

Figure 4: Average correlation during navigation for

different learning thresholds

Figure 3: Evolution of correlation during navigation

CONTINUOUS NAVIGATION OF A MOBILE ROBOT WITH AN APPEARANCE-BASED APPROACH

445

multiplied by the correlation of the current left

image with the matched left one, and the same with

the right offset. This schema can be useful when the

images of each camera are quite different or when

there is an obstacle or occlusion that affects just to

one of the cameras. In these cases, the control action

of the camera that has the problem will be multiplied

by a very low quantity, so it will have a poor effect

on the robot navigation. As well, the experiments

that have been carried out show how this control

schema improves slightly the results that offers the

differential one. These results are shown in fig. 5.

3 CONCLUSIONS AND FUTURE

WORK

A solution to the problem of the continuous

navigation using an appearance-based approach has

been proposed. Several control schemas have been

tested, including P, PD and PD with variable

parameters controllers. With these laws, the robot is

able to find itself and follow the route in a band of

about two meters around the pre-recorded route. It

can be done although the scene suffers small

changes (illumination, position of some objects,

partial occlusions in one of the cameras). We are

now working in other control methods, such fuzzy

logic.

The main drawback of this navigation method

arises when the scenes are highly unstructured and

varying. In this case, it is necessary to increase

resolution to get an acceptable accuracy in

navigation. The solution proposed is based in the

reduction of the information to store using PCA

subspaces. This method shows two big advantages:

the size of the vectors to compare is much smaller

and we can calculate the majority of the information

off-line so we have it available during navigation.

Besides, the size of the vectors is independent of the

resolution of the images so, it is expected to work

well in very unstructured environments.

ACKNOWLEDGEMENTS

This work has been supported by Ministerio de

Educación y Ciencia through project DPI2004-

07433-C02-01. ‘Herramientas de teleoperación

Colaborativa. Aplicación al Control cooperativo de

Robots’.

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Figure 5: Average correlation during navigation for

different control schemes. (a) P controller with K

= K

0.04. (b) P controller with derivative effect in advance

speed. K

= 0.04. (c) PD controller with K

= 0.04 and

= 0.04. (d) PD controller with variable parameters, K

= 0.04 and K

= 0.04

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