Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure’s displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems.

基于视频的测量在模态分析和无损评估方面受到越来越多的关注，在下一代结构传感技术的发展中发挥着重要作用。随着这些技术的发展，更多基于计算机视觉技术的定量方法已经出现在全场无监督结构识别中，利用了使用摄像机所提供的好处，例如高空间传感器密度和低安装成本。最近的工作已经开始探索使用激光点云数据进行场景和结构的 3D 映射。诸如激光雷达之类的传感器以高空间分辨率提供大量测量值，从中可以为诸如生成 CAD 模型之类的应用估计准确的结构几何形状。不幸的是，迄今为止，激光雷达和其他能够进行 3D 几何测量的传感器的帧速率和深度分辨率不足以测量结构动力学。在本文中，我们介绍了一种从使用商业、低成本、飞行时间成像仪获取的点云数据进行高效和极高分辨率 3D 结构动态识别/模态分析的方法。通过基于点云参数创建虚拟拉格朗日传感器，从这些数据中提取振动模式形状和模态坐标。首先，从振动结构中收集时变点云数据。然后，基于动态点云数据创建虚拟传感器网格，用于跟踪结构随时间的位移。盲源分离问题的下一个解决方案用于估计高分辨率 3D 模式形状，模态坐标和共振频率。我们展示了我们提出的实验室测试方法的潜力，并将结果与​​从传统激光位移传感器收集的数据进行了比较。该技术代表了有效探索将动态点云数据用于实际监控应用的全部优势的进步，并且有可能扩展到广泛的 3D 运动分解问题。