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Daniel Meyer-Delius
Mobile robots differentiate themselves from
other types of robots in being able to go from one place to
another in order to execute a given task. During the last dec
ade, mobile robots have performed successfully in a wide range
of different environments such as indoor, outdoor, underwater,
and even on other planets. For most rob otic applications a
model of the environment is a fundamental part of the system. A
representation of how the world looks like is necessary for
performing basic task s such as localization, path planing, and
exploration. Without a model of the environment those tasks
would be impossible, limiting the practical applications of suc
h a robot.
The way in which the environment is represented has an important
impact on the performance of the robot. Accurate maps are fundamental
for navigation. One way to des cribe the environment is to use a
detailed geometrical description of all the objects in it. These
spatial representations can be very accurate and are well suited f or
various important tasks like motion control and accurate
localization. A fundamental question when representing the environment
geometrically is the choice of geo metrical primitive to be
used. Using lines, for example, imposes a linear structure on the
underlying environment. This is well suited for some environments such
as an office, but can be inappropriate for others. Points are the most
general geometrical primitive. Using points allows different
environments to be accurately repres ented without imposing any
geometrical structure on them.
To construct a map, the information about the environment
perceived by the robot is used. This information can be, for example,
the distance to the objects detected by the robot's sensors while
moving through the environment. Using the distance measurements
directly in the way they are produced by the sensors is
straight-forward and general since it does not rely on the environment
having some specific features. By converting these measurements into a
set of points in an absolut coordinate system a \emph{sample-based}
map is constructed. Such a map constitutes a point-based geometrical
representation of the environment where each point or sample corres
ponds to a measurement made by the robot. Thus, beside their accuracy
and generality, sample-based maps are also consistent with the
observations.
This thesis investigates the idea of using samples to model the
environment and presents different techniques for generating
sample-based maps from the distance meas urements acquired by the
robot. We seek to find an efficient representation to accurately
describe the environment. Obviously, if all the measurements acquired
by th e robot are used, the resulting map would be the best
representation of the environment given that data. Distance
measurements, however, come in large amounts and ma y lead to too
large models. Additionally, not every sample contributes in the same
way to the representation, and we may be interested in representing
the environmen t using fewer samples. Thus, our goal is to find a
subset of the complete dataset to efficiently represent the
environment.
The contribution of this thesis are the various approaches to
generate sample-based geometrical maps from range measurements
gathered with a mobile robot as an effic ient representation of the
environment. Sample-based maps are general in the sense that they are
not limited to a specific type of environment and by using points as
primitives for the representation do not impose any structure to the
environment that is being represented. Additionally, sample-based maps
are consistent with the data since they do not contain spurious
points. Every point in a sample based map can be explained by an
existing measurement.
@MastersThesis{meyerdelius2006thesis, author = {Meyer-Delius, D.}, title = {Learning Sample-Based Maps for Mobile Robots}, school = {University of Freiburg, Department of Computer Science}, year = 2006 } PDF-File: master thesis, 1.3 MB pdf file |