Augmented reality-enhanced structural inspection using aerial robots

Augmented reality-enhanced structural inspection using aerial robots
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使用空中机器人进行增强现实增强结构检查

DOI:
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发表时间:
2016
期刊:
International Symposium on Intelligent Control
影响因子:
--
通讯作者:
K. Alexis
K. Alexis
中科院分区:
--
文献类型:
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作者:
C. Papachristos;K. Alexis

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本文研究了自动化路径规划的空中机器人结构检测相结合的增强现实接口,提供实时饲料的立体视图融合实时三维重建数据的环境,同时允许无缝的飞行适应下一个机器人的观点,使用直观的头部运动时所产生的潜力。所提出的解决方案旨在解决结构和环境的准确检查和映射的问题,对于这些结构和环境,存在先验模型,但是先验模型不准确、可能过时或者不对重要特征和语义(诸如人类可读指示和其他纹理信息)进行编码。为了解决该问题,机器人在给定环境的任何先验知识的情况下计算优化的检查路径,而人类操作员利用实况相机视图和实时导出的3D地图数据来局部调整机器人的参考轨迹,使得其访问更新的视点集合,该视点集合提供对真实的环境的期望覆盖以及对某些特征和细节的足够关注。一个自主的空中机器人能够在GPS拒绝的环境中导航和映射,并结合增强现实接口,实验证明了该方法在结构检测应用中的潜力。
This paper investigates the arising potential when automated path planning for aerial robotic structural inspection is combined with an Augmented Reality interface that provides live feed of stereo views fused with real-time 3D reconstruction data of the environment, while allowing seamless on-the-fly adaptation of the next robot viewpoints using intuitive head motions. The proposed solution aims to address the problem of accurate inspection and mapping of structures and environments for which a prior model exists but is not accurate, potentially outdated, or does not encode important features and semantics such as human-readable indications and other texture information. To approach the problem, the robot computes an optimized inspection path given any prior knowledge of the environment, while the human operator utilizes the live camera views and the real-time derived 3D map data to locally adjust the reference trajectory of the robot, such that it visits an updated set of viewpoints which provides the desired coverage of the real environment and sufficient focus on certain features and details. An autonomous aerial robot capable of navigation and mapping in GPS-denied environments is employed and combined with the Augmented Reality interface to experimentally demonstrate the potential of the approach in structural inspection applications.
DOI: 10.1007/s10514-012-9321-0
发表时间: 2013-04-01
期刊: AUTONOMOUS ROBOTS
影响因子: 3.5
作者:
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者: Burgard, Wolfram