Intelligent Small Unmanned Mobile Mapping Systems (i-SUMMS)

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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.