Authonomous Domestic Navigation
Simultaneous Localization and Mapping for Navigation in Realistic Environments
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Navigating autonomously in a domestic environment isa problem that has attracted a great deal of interestin mobile robotics. A robotic systemthat operates in ordinary furnished rooms without theneed of an engineered environment has many differentapplications such as service, cleaning andsurveillance tasks or simply entertainment. This bookdeals with the problem of SimultaneousLocalization and Mapping (SLAM). The mobile robotbuilds a map of an unexplored environment whilesimultaneously using this map to localizeitself. The feature based approach used in this bookutilizes the Extended Kalman Fil...
Navigating autonomously in a domestic environment is
a problem that has attracted a great deal of interest
in mobile robotics. A robotic system
that operates in ordinary furnished rooms without the
need of an engineered environment has many different
applications such as service, cleaning and
surveillance tasks or simply entertainment. This book
deals with the problem of Simultaneous
Localization and Mapping (SLAM). The mobile robot
builds a map of an unexplored environment while
simultaneously using this map to localize
itself. The feature based approach used in this book
utilizes the Extended Kalman Filter (EKF) machinery
to estimate the pose of the robot and the location of
the features. This approach is referred to as
stochastic mapping.
In addition, this book explores a method for
recovering from the most common mode of failure of
the stochastic mapping approach.
Finally, the book presents a method for achieving
more accurate navigation by using the architectural
properties of most domestic environments.
All the algorithms presented in this book have been
tested and verified in real world experiments.
a problem that has attracted a great deal of interest
in mobile robotics. A robotic system
that operates in ordinary furnished rooms without the
need of an engineered environment has many different
applications such as service, cleaning and
surveillance tasks or simply entertainment. This book
deals with the problem of Simultaneous
Localization and Mapping (SLAM). The mobile robot
builds a map of an unexplored environment while
simultaneously using this map to localize
itself. The feature based approach used in this book
utilizes the Extended Kalman Filter (EKF) machinery
to estimate the pose of the robot and the location of
the features. This approach is referred to as
stochastic mapping.
In addition, this book explores a method for
recovering from the most common mode of failure of
the stochastic mapping approach.
Finally, the book presents a method for achieving
more accurate navigation by using the architectural
properties of most domestic environments.
All the algorithms presented in this book have been
tested and verified in real world experiments.
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