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the position. However, as no relevant information is
extracted from the images, it is necessary to apply a
compression method to reduce the computational cost
of the mapping and localization processes. Several
researchers have developed DFT (Discrete Fourier
Transform) methods to get the most relevant infor-
mation from the images (Menegatti et al., 2004a).
These descriptors present rotational ground-plane in-
variance, concentrate the most relevant information in
the low frequency components of the transformed im-
ages and each image descriptor is computed indepen-
dently of the rest of images.
For these reasons, and based on some prior works
(Pay´a et al., 2009; Pay´a et al., 2010; Fern´andez et al.,
2010), we have decided to describe each omnidirec-
tional image by means of a Fourier descriptor. We
use the Fourier Signature (Menegatti et al., 2004a) to
compress each image captured. The processing time
needed to compute the Fourier Signature is noticeably
lower than in other common feature extraction algo-
rithms (Pay´a et al., 2009), and it permits a fast com-
parison between the images in the map by means of
a vector distance measurement. Also, when using the
Fourier Signature, we exploit better the invariance to
ground-planerotations in panoramic images, property
that will be of utmost importance when deploying our
visual compass.
In this paper we present a methodology to build
a topological map using the global appearance of the
panoramic images to model the topological relation-
ships between successive nodes in the map. We use
the Fourier Signature to get a robust descriptor that
allow us to work in real-time. However, the meth-
ods described here are independent of the descriptor
used to represent the images, and other appearance-
based descriptors may also be applied. To represent
the distance between two consecutive poses we have
used the normalized Euclidean distance between their
Fourier Signatures and to get the relative angle be-
tween them we have implemented a Fourier-based vi-
sual compass. Our main objective consists in evaluat-
ing the feasibility of using purely global-appearance
methods in these tasks and how the main features of
the descriptor influence the final result.
The paper is organized as follows. Section 2
presents the fundamentals of topological mapping ap-
proaches. In section 3, we describe the Fourier de-
scriptor and howto use it with omnidirectionalimages
to implement the visual compass. Section 4 deals with
the problem of localization and map creation using
visual odometry. Next, Section 5 presents the exper-
imental setting and the results obtained. Finally, we
present the conclusions and future work in Section 6.
2 TOPOLOGICAL MAP
BUILDING. STATE OF THE ART
With respect to the mapping problem we can establish
two approaches: metric and topological. The first one
consists in modeling the environment using a metric
map obtained with geometrical accuracy when repre-
senting the position of the robot in it. For example
(Gil et al., 2010) present an approach to carry out
the mapping process with a team of mobile robots
and visual information. On the other side, topolog-
ical mapping consists in the creation of maps that
represent graphical models of the environment that
capture places and their connectivity in a compact
form. An example of this approach is presented in
(Pay´a et al., 2010) where a topological representation
of the environment is obtained by applying a method
based on the physics of harmonic oscillators. Also,
(Tully et al., 2009) describe a probabilistic method for
topological SLAM (Simultaneous Localization and
Mapping), solving the topological graph loop-closing
problem. At last, (Fern´andez et al., 2010) describe
a Monte-Carlo Localization using the robot odome-
try and the appearance of omnidirectional images to
localize in a topological map.
Recovering relative robot poses from a set of cam-
era images has been a largely studied problem in re-
cent years. For example (Nist´er et al., 2006) present
a system that estimates the motion of a stereo head
using a feature tracker or (Scaramuzza and Siegwart,
2008) describe a real-time algorithm for computing
the ego-motion of a vehicle using as input only om-
nidirectional images and (Scaramuzza et al., 2010)
study how to close the loop by using the omnidirec-
tional visual odometry and a vocabulary tree. This
work shows how it is possible to carry out a process
of robot localization and mapping simultaneously us-
ing as input data only omnidirectional images and a
loop-closing process.
In this paper, we face the mapping problem as a
relative camera pose recovering problem, using the
overall appearance of the panoramic images, without
any feature extraction process. We describe a real-
time algorithm for computing an appearance-based
topological map through visual odometry. The main
contributions of the work are the development of a vi-
sual compass that permits computing the position and
orientation of each new location in the map, with a
low computational cost.
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