Reference number : GC-2017-Q1-00295, amendment number : , agreement type : G, recipient legal name : Lichti, Derek (University of Calgary), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : Mobile mapping systems (MMSs) are three-dimensional reality capture systems that collect georeferenced spatial data with integrated navigation and imaging sensors from a moving vehicle (terrestrial or aerial). MMS data are a part of everyday life. Google Street View and Google Earth View are the best-known examples of how MMS technology is used by the general population. The applications of MMS span many discipline areas including power line mapping, infrastructure monitoring and industrial site modelling. Models constructed from MMS data allow users to virtually inspect sites and derive measurements in support of informed engineering decisions. This project focuses on solving two industry-relevant, scientific problems that will enhance the accuracy and the utility of MMS data models. The first is automated MMS calibration. The accuracy of extracted measurements is critically dependent on precise knowledge of the relative location and orientation of the various sensors on board the MMS, which is calibrated prior to data acquisition. The orientation is known to be unstable over time, which compromises system accuracy. A new method for automated system calibration using objects inherent to an MMS (e.g. poles) will be developed to allow new orientation parameters to be solved as the vehicle is driven for data collection. The second is automated image registration. Imagery of a site is often collected long after MMS data capture in order to perform change detection. In this situation the imagery may be collected from a smart phone and its orientation is unknown. New methods will be developed to automatically orient this imagery to already-collected MMS data so that site models can be updated., program purpose french : Mobile mapping systems (MMSs) are three-dimensional reality capture systems that collect georeferenced spatial data with integrated navigation and imaging sensors from a moving vehicle (terrestrial or aerial). MMS data are a part of everyday life. Google Street View and Google Earth View are the best-known examples of how MMS technology is used by the general population. The applications of MMS span many discipline areas including power line mapping, infrastructure monitoring and industrial site modelling. Models constructed from MMS data allow users to virtually inspect sites and derive measurements in support of informed engineering decisions. This project focuses on solving two industry-relevant, scientific problems that will enhance the accuracy and the utility of MMS data models. The first is automated MMS calibration. The accuracy of extracted measurements is critically dependent on precise knowledge of the relative location and orientation of the various sensors on board the MMS, which is calibrated prior to data acquisition. The orientation is known to be unstable over time, which compromises system accuracy. A new method for automated system calibration using objects inherent to an MMS (e.g. poles) will be developed to allow new orientation parameters to be solved as the vehicle is driven for data collection. The second is automated image registration. Imagery of a site is often collected long after MMS data capture in order to perform change detection. In this situation the imagery may be collected from a smart phone and its orientation is unknown. New methods will be developed to automatically orient this imagery to already-collected MMS data so that site models can be updated., agreement title english : New registration techniques to improve mobile lidar mapping accuracy, agreement title french : New registration techniques to improve mobile lidar mapping accuracy, agreement number : CRDPJ, agreement value : 111137.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2021-2022), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2021-2022), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada