NRI: Collaborative Research: Autonomous Quadrotors for 3D Modeling and Inspection of Outdoor Infrastructure

NRI：协作研究：用于室外基础设施 3D 建模和检查的自主四旋翼飞行器

• 批准号: 1637875
• 负责人: Junaed Sattar
• 金额: $ 83.03万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637875&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Autonomous; Quadrotors

This project develops technologies to collect visual and inertial data necessary for constructing, offline, high-accuracy 3D maps of the structure for civil and industrial infrastructure such as bridges, power plants, and refineries. It also develops technologies for online processing including localization, path planning and obstacle avoidance. The project builds a system that employs quadrotors to assist their human co-workers in visual inspections of the outdoor infrastructure to enhance efficiency and effectiveness of such operations. The research advances the current state of the art in key areas of sensing, estimation, and control necessary for enabling small-size quadrotors to assist humans in visual inspections. In addition to improving the reliability of the nation's infrastructure, the project benefits researchers, developers, educators, and end-users in robotics by developing open-source, modular algorithms for quadrotors. The project offers educational and community outreach activities aligned with local efforts and state-wide initiatives, and seeks to increase diversity and attract underrepresented groups to Science, Technology, Engineering, and Mathematics (STEM) via a partnership with local high schools. This research addresses the fundamental challenges stemming from sensing and processing limitations that prevent the use of low-cost, small-size quadrotors in visual-inspection tasks. It focuses on a four-step process, where initially a quadrotor is tele-operated at a safe distance from the structure of interest to collect visual and inertial data necessary for constructing, offline, high-accuracy 3D maps of the structure. These maps are then used, by the inspection engineer, to designate areas of interest. Lastly, the quadrotor employs its onboard sensors to precisely localize with respect to the structure and navigate along the inspection route, while collecting additional data for increasing the accuracy and improving the reliability of future inspections. A key innovation is making information available in multiple forms and levels of abstraction so as to meet the often-conflicting needs of offline (e.g., visualization of inspection areas and planning information-rich paths) and online (e.g., map-based localization and obstacle avoidance) uses. Also critical is an information-driven approach for making maximum use of the limited sensing and processing resources available to the quadrotor. Lastly, a key advantage of the proposed approach is that it provides the foundation for continual improvement in accuracy and efficiency after each inspection flight.

该项目开发技术，以收集必要的视觉和惯性数据，用于构建桥梁，发电厂和炼油厂等民用和工业基础设施的离线高精度3D结构地图。 它还开发在线处理技术，包括定位，路径规划和避障。该项目构建了一个系统，该系统采用四旋翼来协助其人类同事对户外基础设施进行目视检查，以提高此类操作的效率和有效性。 该研究推进了传感，估计和控制的关键领域的当前技术水平，这些领域是使小型四旋翼能够协助人类进行视觉检查所必需的。除了提高国家基础设施的可靠性外，该项目还通过开发四旋翼的开源模块化算法，使机器人领域的研究人员、开发人员、教育工作者和最终用户受益。该项目提供与当地努力和全州范围内的倡议相一致的教育和社区外联活动，并寻求通过与当地高中的伙伴关系增加多样性并吸引代表性不足的群体参与科学，技术，工程和数学（STEM）。这项研究解决了来自传感和处理限制的根本挑战，这些限制阻止了在视觉检测任务中使用低成本，小尺寸四旋翼。它侧重于一个四步过程，其中最初在离感兴趣结构的安全距离处远程操作四旋翼，以收集构建结构的离线高精度3D地图所需的视觉和惯性数据。然后，这些地图由检查工程师用于指定感兴趣的区域。最后，四旋翼飞行器利用其机载传感器精确定位结构，并沿检测路线沿着导航，同时收集额外数据，以提高未来检测的准确性和可靠性。一个关键的创新是使信息以多种形式和抽象层次可用，以满足离线（例如，检查区域的可视化和规划信息丰富的路径）和在线（例如，基于地图的定位和避障）用途。同样关键的是信息驱动的方法，以最大限度地利用有限的传感和处理资源提供给四旋翼。最后，所提出的方法的一个主要优点是，它为每次检查飞行后不断提高准确性和效率奠定了基础。

项目成果

Deep Multi-view Depth Estimation with Predicted Uncertainty

• DOI: 10.1109/icra48506.2021.9560873
• 发表时间: 2020-11
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Tong Ke;Tien Do;Khiem Vuong;K. Sartipi;S. Roumeliotis

Fast Direct Stereo Visual SLAM

快速直接立体视觉 SLAM

• DOI: 10.1109/lra.2021.3133860
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Mo, Jiawei;Islam, Md Jahidul;Sattar, Junaed
• 通讯作者: Sattar, Junaed

Continuous-Time Spline Visual-Inertial Odometry

连续时间样条视觉惯性里程计

• 发表时间: 2022
• 期刊: Proiceedings of the 2022 International Conference on Robotics and Automation (ICRA
• 作者: Jiawei Mo;Junaed Sattar
• 通讯作者: Junaed Sattar

IMU-Assisted Learning of Single-View Rolling Shutter Correction

IMU辅助单视卷帘快门校正学习

• 发表时间: 2021
• 期刊: Conference on Robot Learning
• 作者: Islam, Md J.;Sattar, J.
• 通讯作者: Sattar, J.

A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors

使用 LiDAR 描述符的立体视觉里程计的快速、稳健的地点识别方法

• DOI: 10.1109/iros45743.2020.9341733
• 发表时间: 2020
• 期刊: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Mo, Jiawei;Sattar, Junaed
• 通讯作者: Sattar, Junaed