Fusion of terrestrial and UAV-based point clouds for precision mapping and infrastructure inspection

融合地面和无人机点云，用于精确测绘和基础设施检查

• 批准号: 530963-2018
• 负责人: ElRabbany, Ahmed
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650129
• 关键词: Fusion; terrestrial; UAV; based; point

At present, either of the terrestrial laser scanners (TLS) or unmanned aerial vehicle (UAV)-based**LiDAR/camera systems are used for precision mapping and infrastructure inspection. Unfortunately, however,**both systems suffer from drawbacks, including the lower spatial resolution and complexity of sensor**integration of the UAV-based systems and the mobility and inability to scan dangerous-to-reach areas of TLS.**This research project aims to optimally fuse the point cloud data from both of the TLS and UAV-based**LiDAR/camera systems, which has the potential to overcome the drawbacks of either system alone.**The research will first select various sites representing different operational scenarios (e.g., bridges, dense**vegetation, buildings) for data collection with both of the FARO TLS and UAV-based systems. A number of**ground control points and targets will be established to enhance the image acquisition and accuracy of the point**cloud registration. As a low-cost digital camera will be used onboard the UAV, an accurate and efficient**camera calibration method for all possible operational scenarios will be developed, including the determination**interior orientation elements and distortion parameters. As well, LiDAR data will be corrected for both of the**geometric and radiometric effects. Trimble POSPac UAV software package will be used for post-processing of**the GNSS/MEMS IMU data and for geo-referencing of the imaging and LiDAR data. Pix4D and Autodesk**ReCap Pro software packages will be used to verify the image registration, along with MATLAB and**CloudCompare software. Both of the TLS and UAV-based point cloud data will finally be used to create a**complete 3D model of the scanned structure and surroundings.**The outcome of this research will significantly improve the efficiency of the field workflow, improve the**positioning accuracy, and enhance the inspection and risk assessment procedures of Canada's infrastructure.**This in turn helps making informed decision about infrastructure service and repair.

目前，地面激光扫描仪（TLS）或基于无人机（UAV）的激光雷达/相机系统都用于精密测绘和基础设施检查。然而，不幸的是，** 这两种系统都有缺点，包括较低的空间分辨率和传感器的复杂性 ** 基于无人机的系统的集成以及移动性和无法扫描TLS的难以到达的区域。该研究项目旨在最佳地融合来自TLS和基于无人机的激光雷达/相机系统的点云数据，这有可能克服任何一个系统的缺点。研究将首先选择代表不同操作场景的各种站点（例如，桥梁、茂密的植被、建筑物），以便使用FARO TLS和基于无人机的系统进行数据收集。建立多个地面控制点和目标，以提高图像采集和点云配准的精度。由于无人机上将使用低成本的数码相机，因此将开发一种适用于所有可能的操作场景的准确有效的 ** 相机校准方法，包括确定 ** 内部方位元素和畸变参数。此外，LiDAR数据将针对 ** 几何和辐射效应进行校正。特林布尔POSPac无人机软件包将用于 ** GNSS/MEMS IMU数据的后处理以及成像和LiDAR数据的地理参考。Pix 4D和Autodesk**ReCap Pro软件包将用于验证图像配准，沿着MATLAB和 **CloudCompare软件。TLS和基于无人机的点云数据最终将用于创建扫描结构和周围环境的完整3D模型。这项研究的成果将大大提高现场工作流程的效率，提高 ** 定位精度，并加强加拿大基础设施的检查和风险评估程序。这反过来又有助于对基础设施服务和维修做出明智的决定。