3D Mobile Mapping Using Artificial Intelligence

关键基础设施，公用事业，交通和设施的相互依赖的网络，是加拿大经济和社会的支柱。虽然加拿大是世界第二大国家，是世界第十大经济体，但其三分之一的基础设施需要重大更新。必须进行定期、严格的监测，以确保基础设施的安全和有效运作。然而，当前以人为中心的检查实践不足以满足这一需求，而且很费力。需要能够进行连续和准确监测的廉价和更有效的技术。3D移动的测绘系统（MMS）是一种新兴的解决方案，其可以收集视觉传感数据以使用公共移动平台高速地测量整个基础设施。 在获取传感数据以生成3D点云方面取得了重大进展;然而，这些数据集的采集后处理仍然是成功生成3D高保真地图的挑战，这些地图在语义上可解释，几何上详细，并具有测量等级精度的地理参考。Teledyne Optech总部位于多伦多，是MMS领域的全球领导者。NSERC CRD项目将与Teledyne Optech合作，使用一种称为深度神经网络的特定类型的人工智能（AI）开发一种先进的数据处理系统，该系统最近在计算机和机器人视觉以及机器学习方面取得了显着的成功。这项工作将允许使用MMS数据和关键网络的高质量3D模型自动识别基础设施资产，从而为基础设施管理领域做出贡献，并提高整个城市的可持续性。所开发的技术将能够与以人为中心的技术竞争，并减少采集后数据处理所需的时间;这些技术将便利MMS在没有GPS的环境中的操作，并使用户能够根据需要真实的调整勘测参数。通过该计划培训的HQP将为加拿大各行业以及人工智能技术，基础设施管理，城市规划和MMS领域做出贡献。

Critical infrastructure, the interdependent networks of utilities, transportation, and facilities, are the backbone of Canada's economy and society. Although Canada is the 2nd largest country in the world, with the world's 10th largest economy, one-third of its infrastructure is in need of a significant update. Regular, rigorous monitoring is necessary to ensure the safe and efficient operation of the infrastructure. However, current human-centric inspection practices are inadequate and laborious to fulfill this need. Inexpensive and more efficient technologies that are capable of continuous and accurate monitoring are necessary. 3D mobile mapping system (MMS) is an emerging solution that can collect visual sensory data to survey entire infrastructure with high speed using a common moving platform. There have been major advancements in acquiring sensory data to produce 3D point clouds; however, the post-acquisition processing of these datasets remains a challenge to successfully generate 3D high-fidelity maps, which are semantically interpretable, geometrically detailed and georeferenced with survey grade accuracy. Teledyne Optech, headquartered in Toronto, is a world leader in the MMS field. In collaboration with Teledyne Optech, this NSERC CRD project will develop an advanced data processing system using a specific type of artificial intelligence (AI), called deep neural network, which has recently achieved remarkable success in computer and robotic vision and machine learning. This work will allow for the autonomous recognition of infrastructure assets using the MMS data and high-quality 3D models of critical networks, thus contributing to the field of infrastructure management and improving urban sustainability as a whole. The technologies developed will be able to compete with human-centric technique and reduce the time required for post-acquisition data processing; they will facilitate the operation of the MMS in GPS-denied environments and allow users to adjust survey parameters as needed in real time. The HQP trained through this program will contribute to various Canadian industries and the fields of AI technologies, infrastructure management, urban planning, and MMS.