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This book focuses on enabling mobile robots to recognize scenes in
indoor environments, in order to allow them to determine which
actions are appropriate at which points in time. In concrete terms,
future robots will have to solve the classification problem
represented by scene recognition sufficiently well for them to act
independently in human-centered environments. To achieve accurate
yet versatile indoor scene recognition, the book presents a
hierarchical data structure for scenes - the Implicit Shape Model
trees. Further, it also provides training and recognition
algorithms for these trees. In general, entire indoor scenes cannot
be perceived from a single point of view. To address this problem
the authors introduce Active Scene Recognition (ASR), a concept
that embeds canonical scene recognition in a decision-making system
that selects camera views for a mobile robot to drive to so that it
can find objects not yet localized. The authors formalize the
automatic selection of camera views as a Next-Best-View (NBV)
problem to which they contribute an algorithmic solution, which
focuses on realistic problem modeling while maintaining its
computational efficiency. Lastly, the book introduces a method for
predicting the poses of objects to be searched, establishing the
otherwise missing link between scene recognition and NBV
estimation.
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