文章基本信息

标题：Volumetric Next-best-view Planning for 3D Object Reconstruction with Positioning Error
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作者：J. Irving Vasquez-Gomez ; L. Enrique Sucar ; Rafael Murrieta-Cid 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2014
卷号：11
DOI：10.5772/58759
语种：English
出版社：SAGE Publications
摘要：Three-dimensional (3D) object reconstruction is the process of building a 3D model of a real object. This task is performed by taking several scans of an object from different locations (views). Due to the limited field of view of the sensor and the object’s self-occlusions, it is a difficult problem to solve. In addition, sensor positioning by robots is not perfect, making the actual view different from the expected one. We propose a next best view (NBV) algorithm that determines each view to reconstruct an arbitrary object. Furthermore, we propose a method to deal with the uncertainty in sensor positioning. The algorithm fulfills all the constraints of a reconstruction process, such as new information, positioning constraints, sensing constraints and registration constraints. Moreover, it improves the scan’s quality and reduces the navigation distance. The algorithm is based on a search-based paradigm where a set of candidate views is generated and then each candidate view is evaluated to determine which one is the best. To deal with positioning uncertainty, we propose a second stage which re-evaluates the views according to their neighbours, such that the best view is that which is within a region of the good views. The results of simulation and comparisons with previous approaches are presented.
关键词：View Planning; Sensor Planning; Next Best View; Object Reconstruction