18,889 research outputs found
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A Framework for Genetic Algorithms Based on Hadoop
Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in
many real-world applications. The sequential execution of GAs requires
considerable computational power both in time and resources. Nevertheless, GAs
are naturally parallel and accessing a parallel platform such as Cloud is easy
and cheap. Apache Hadoop is one of the common services that can be used for
parallel applications. However, using Hadoop to develop a parallel version of
GAs is not simple without facing its inner workings. Even though some
sequential frameworks for GAs already exist, there is no framework supporting
the development of GA applications that can be executed in parallel. In this
paper is described a framework for parallel GAs on the Hadoop platform,
following the paradigm of MapReduce. The main purpose of this framework is to
allow the user to focus on the aspects of GA that are specific to the problem
to be addressed, being sure that this task is going to be correctly executed on
the Cloud with a good performance. The framework has been also exploited to
develop an application for Feature Subset Selection problem. A preliminary
analysis of the performance of the developed GA application has been performed
using three datasets and shown very promising performance
Acquiring Correct Knowledge for Natural Language Generation
Natural language generation (NLG) systems are computer software systems that
produce texts in English and other human languages, often from non-linguistic
input data. NLG systems, like most AI systems, need substantial amounts of
knowledge. However, our experience in two NLG projects suggests that it is
difficult to acquire correct knowledge for NLG systems; indeed, every knowledge
acquisition (KA) technique we tried had significant problems. In general terms,
these problems were due to the complexity, novelty, and poorly understood
nature of the tasks our systems attempted, and were worsened by the fact that
people write so differently. This meant in particular that corpus-based KA
approaches suffered because it was impossible to assemble a sizable corpus of
high-quality consistent manually written texts in our domains; and structured
expert-oriented KA techniques suffered because experts disagreed and because we
could not get enough information about special and unusual cases to build
robust systems. We believe that such problems are likely to affect many other
NLG systems as well. In the long term, we hope that new KA techniques may
emerge to help NLG system builders. In the shorter term, we believe that
understanding how individual KA techniques can fail, and using a mixture of
different KA techniques with different strengths and weaknesses, can help
developers acquire NLG knowledge that is mostly correct
Exploring the Use of Virtual Worlds as a Scientific Research Platform: The Meta-Institute for Computational Astrophysics (MICA)
We describe the Meta-Institute for Computational Astrophysics (MICA), the
first professional scientific organization based exclusively in virtual worlds
(VWs). The goals of MICA are to explore the utility of the emerging VR and VWs
technologies for scientific and scholarly work in general, and to facilitate
and accelerate their adoption by the scientific research community. MICA itself
is an experiment in academic and scientific practices enabled by the immersive
VR technologies. We describe the current and planned activities and research
directions of MICA, and offer some thoughts as to what the future developments
in this arena may be.Comment: 15 pages, to appear in the refereed proceedings of "Facets of Virtual
Environments" (FaVE 2009), eds. F. Lehmann-Grube, J. Sablating, et al., ICST
Lecture Notes Ser., Berlin: Springer Verlag (2009); version with full
resolution color figures is available at
http://www.mica-vw.org/wiki/index.php/Publication
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
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