There has been an increasing interest in multi-disciplinary
research on multisensor attitude estimation technology driven by
its versatility and diverse areas of application, such as sensor
networks, robotics, navigation, video, biomedicine, etc. Attitude
estimation consists of the determination of rigid bodies'
orientation in 3D space. This research area is a multilevel,
multifaceted process handling the automatic association,
correlation, estimation, and combination of data and information
from several sources. Data fusion for attitude estimation is
motivated by several issues and problems, such as data
imperfection, data multi-modality, data dimensionality, processing
framework, etc. While many of these problems have been identified
and heavily investigated, no single data fusion algorithm is
capable of addressing all the aforementioned challenges. The
variety of methods in the literature focus on a subset of these
issues to solve, which would be determined based on the application
in hand. Historically, the problem of attitude estimation has been
introduced by Grace Wahba in 1965 within the estimate of satellite
attitude and aerospace applications. This book intends to provide
the reader with both a generic and comprehensive view of
contemporary data fusion methodologies for attitude estimation, as
well as the most recent researches and novel advances on
multisensor attitude estimation task. It explores the design of
algorithms and architectures, benefits, and challenging aspects, as
well as a broad array of disciplines, including: navigation,
robotics, biomedicine, motion analysis, etc. A number of issues
that make data fusion for attitude estimation a challenging task,
and which will be discussed through the different chapters of the
book, are related to: 1) The nature of sensors and information
sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer,
etc.); 2) The computational ability at the sensors; 3) The
theoretical developments and convergence proofs; 4) The system
architecture, computational resources, fusion level.
General
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