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[资源] 【分享】Hierarchical Voronoi Graphs.Springer.2010

Hierarchical Voronoi Graphs_Spatial Representation and Reasoning for Mobile Robots
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Authors(Editors):
Jan Oliver Wallgrun
Publisher: Springer
Pub Date: 2010
Pages: 218
ISBN:
ISBN 978-3-642-10302-5 e-ISBN 978-3-642-10345-2
DOI 10.1007/978-3-642-10345-2

Foreword
What is space? Is there space when there are objects to occupy it or is there space only
when there are no objects to occupy it? Can there be space without objects? These
are old philosophical questions that concern the ontology of space in the philosophical
sense of ‘ontology’ – what is the nature of space?
Cognitive science in general and artificial intelligence in particular are less concerned
with the nature of things than with their mental conceptualizations. In spatial
cognition research we address questions like What do we know about space? How
is space represented? What are the representational entities? What are the representational
structures? Answers to these questions are described in what is called
ontologies in artificial intelligence. Different tasks require different knowledge, and
different representations of knowledge facilitate different ways of solving problems.
In this book, Jan OliverWallgrün develops and investigates representational structures
to support tasks of autonomous mobile robots, from the acquisition of knowledge to
the use of this knowledge for navigation.
The research presented is concerned with the robot mapping problem, the problem
of building a spatial representation of an environment that is perceived by sensors
that only provide incomplete and uncertain information; this information usually
needs to be related to other imprecise or uncertain information. The routes a robot
can take can be abstractly described in terms of graphs where alternative routes are
represented by alternative branches in these route graphs. The proposed hierarchical
Voronoi graph representation provides a conceptualization of space excellently suited
for navigation, high-level spatial reasoning about environments, and agent-agent communication
about navigable space. The problem is that the Voronoi representations are
rather sensitive to measurement errors; therefore unique representations cannot easily
be derived in practice. In this book, Jan Oliver Wallgrün presents a number of significant
contributions to the state of the art in robot mapping by developing techniques for
generating stable Voronoi representations from real sensor data.
Robot mapping and the construction of Voronoi graphs can also be seen as a spatial
reasoning problem in which local spatial knowledge has to be consistently integrated
into global knowledge. On an abstract qualitative level, these kinds of spatial reasoning
problems are studied in the area of qualitative spatial reasoning. Jan Oliver
Wallgrün successfully brings together these two distinct research areas; this results in
a multi-hypothesis tracking framework for topological mapping. From the perspective
of qualitative spatial reasoning, the work describes an instructive application domain
for comparing spatial calculi. It also provides valuable results regarding the applicability
of direction information and existing reasoning techniques. The research presented
in this book raises significant challenges for the development of spatial calculi and
spatial reasoning systems that have to be addressed by future theoretical research.
Bremen, Christian Freksa
July, 2009

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