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Accurate wide-area tracking for architectural, engineering and surveying applications.

机译:针对建筑,工程和测量应用的精确广域跟踪。

摘要

Augmented Reality (AR) is a powerful tool for the visualisation of, and interaction with, digital information, and has been successfully deployed in a number of consumer applications. Despite this, AR has had limited success in industrial applications as the combined precision, accuracy, scalability and robustness of the systems are not up to industry standards. With these characteristics in mind, we present a concept Industrial AR (IAR) framework for use in outdoor environments.Within this concept IAR framework, we focus on the improving the precision and accuracy of consumer level devices by focusing on the issue of localisation, utilising LiDAR based point clouds generated as part of normal surveying and engineering workflow.We evaluate key design points to optimise the localisation solution, including the impact of increased field of view on feature matching performance, the filtering of feature matches between real imagery and an observed point cloud, and how pose can be estimated from 2D to 3D point correspondences.The overall accuracy of this localisation algorithm with respect to ground-truth observations is determined, with unfiltered results indicating an on par horizontal accuracy and significantly improved vertical accuracy with best-case consumer GNSS solutions. When additional filtering is applied, results of localisation show a higher accuracy than best-case consumer GNSS.
机译:增强现实(AR)是一种功能强大的工具,可用于可视化数字信息并与数字信息进行交互,并且已成功部署在许多消费者应用程序中。尽管如此,由于系统的综合精度,准确性,可扩展性和鲁棒性不符合行业标准,因此AR在工业应用中取得的成功有限。考虑到这些特性,我们提出了一个用于室外环境的概念工业AR(IAR)框架。在此概念IAR框架中,我们专注于通过关注本地化问题,提高消费级设备的精度和准确性。基于LiDAR的点云是正常勘测和工程工作流程的一部分,我们评估关键设计点以优化定位解决方案,包括增加视野对特征匹配性能的影响,真实图像与观察点之间的特征匹配过滤云,以及如何从2D到3D点的对应关系估计姿态。确定了该定位算法相对于地面真相观测的整体精度,未经过滤的结果表明水平精度相同,并且在最佳情况下垂直精度得到显着提高消费者GNSS解决方案。当应用附加过滤时,定位结果显示出比最佳情况下的消费者GNSS更高的准确性。

著录项

  • 作者

    Head-Mears James Bradley;

  • 作者单位
  • 年度 2013
  • 总页数
  • 原文格式 PDF
  • 正文语种 en
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