Intelligent Small Unmanned Mobile Mapping Systems (i-SUMMS)

移动地图系统(MMS)通过移动平台上集成的导航和地图传感器来收集空间数据。平台、推进、控制、导航、自动驾驶、电源、传感器、微处理器、无线通信、物联网和传感器小型化等学科和技术的进步导致了小型无人机(UAV)和无人地面飞行器(UGV)移动测绘平台的发展。目前，无人机和UGV大多是远程操作或以预先计划的自动化模式操作。这一研究方案的目标是开发创新办法，利用智能小型无人移动测绘系统(I-SUMMS)处理不断增加的需要地理空间数据的应用，如高清晰度测绘；复杂环境中的导航和测绘；视线以外的测绘任务；航空和地面遥感空间数据的整合；多传感器、多平台作业；实时和在线测绘；以及空间变化检测。将研究自动和自主传感器定位以及3D高清晰度几何、拓扑和语义映射的方法。路线规划和障碍物感知和避障的关键要素将被整合，因为i-SUMMS将通过使用视觉和主动传感器的探索和理解来“学习”。将研究无人机与UGV、移动车辆与地面站之间的协作和协同方法。由于所涉及的数据集很大，因此将对机器学习和深度学习的方法和算法进行研究和实现。这项研究计划的创新之处在于为自动化和自主的地理空间应用开发I-SUMMS，从而为新兴的机器人移动地图绘制领域做出贡献。在动态的物理环境中运行，智能系统的性能取决于适应物理世界变化的程度和时间，I-SUMMS将如何处理任何有限和/或不准确的数据，感知世界转换为数据和信息的效果如何，以及计算执行的效率如何。与单个小型无人机或UGV MMS相比，多个I-SUMMS由于协同作战，有望更有效地完成各种复杂的地球测量任务。该计划将对自动驾驶汽车的物理孪生环境和基础设施的产生做出重大贡献。到2023年，全球移动地图市场的价值将达到320亿美元，预计将有越来越多的地理空间应用需要使用i-SUMMS。在该项目中培训的HQP将成为自动化和自主测绘领域的科技领导者，他们对学术界、工业界和政府的贡献将为加拿大带来巨大的社会经济效益，并推动加拿大在该领域的国际领先地位。

Mobile mapping systems (MMS) collect spatial data by the means of integrated navigation and mapping sensors onboard a mobile platform. Advances in disciplines and technologies, such as platform, propulsion, control, navigation, autopilots, power, sensor, microprocessors, wireless communications, IoT, and miniaturization of sensors led to the development of small Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV) mobile mapping platforms. Currently UAV and UGV are mostly operated remotely or in preplanned automated modes. The objective of this research program is the development of innovative approaches for employing intelligent small unmanned mobile mapping systems (i-SUMMS) to handle the continuous increase of applications requiring geospatial data, such as high definition mapping; navigation and mapping in complex environments; mapping missions beyond line of sight; integration of aerial and terrestrial sensing spatial data; multi-sensor, multiplatform operations; on-time and on-line mapping; and spatial change detection. Approaches for automated and autonomous sensor localization and 3D high definition geometric, topological and semantic mapping will be investigated. The critical elements of route planning and obstacle sense-and-avoidance will be integrated as the i-SUMMS will "learn" through exploration and understanding using vision and active sensors. Collaborative and synergistic approaches between UAV and UGV, and between the moving vehicle and the ground station will be studied. Due to the large data sets involved, machine learning and deep learning methods and algorithms will be investigated and implemented. The novelty of this research program is on the development of i-SUMMS for automated and autonomous geospatial applications, thus contributing to the emerging field of robotic mobile mapping. Operating in dynamic physical environments, the performance of intelligent systems depends on how well and timely the changes of the physical world are accommodated, how the i-SUMMS will handle any limited and /or inaccurate data, how well the sensing world is transformed into data and information and how efficiently the computations are performed. Compared to single small UAV or UGV MMS, multiple i-SUMMS are expected to be more effective in completing various complicated geomatics tasks due to the synergistic operations. This program will significantly contribute to the generation of physical twin environments and the infrastructure for autonomous car driving. With the value of the global mobile mapping market reaching US$32 billion by 2023 it is expected that more and more geospatial applications will require the use of i-SUMMS. The HQP trained in this program will be the scientific and technological leaders in automated and autonomous mapping and their contributions to academia, industry and governments will result in tremendous socio-economic benefits to Canada and propel Canada's international leadership in this field.