Affiliation:
1. National Institute for Astrophysics Optics and Electronics, Puebla, Mexico
2. Center for Mathematical Research, Guanajuato, Mexico
3. Robert Bosch, Mexico
Abstract
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.
Subject
Artificial Intelligence,Computer Science Applications,Software
Cited by
85 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献