Indoor scene recognition by 3-D object search : for robot programming by demonnstration
著者
書誌事項
Indoor scene recognition by 3-D object search : for robot programming by demonnstration
（Springer tracts in advanced robotics, 135）
Springer, c2020
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注記
"Dissertation approved by the KIT Department of informatics. Oral examination on July 6th,2018 at Karlsruhe institute of Technology (KIT)"--T.p. verso
Includes bibliographical references
内容説明・目次
内容説明
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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