Planning for terrestrial laser scanning in digital construction
Terrestrial Laser Scanning (TLS) is a reliable method for collecting point clouds. Two very important aspects in capturing point clouds using TLS are “effectiveness” and “efficiency”. Effective and efficient data collection can be achieved through a priori planning. Effectiveness deals with quality of the dataset i.e. considering the criteria to ensure that the acquired point clouds are appropriate for the given task. Completeness, accuracy, and points’ density are the main criteria in this regard. Efficiency of data collection mainly copes with the time which is required for scanning the object of interest. This is directly related to number of stations (scan positions) and is also constrained with quality parameters and environmental aspects.
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Towards a combination between UAV-image block and depth-maps data from smartphones
UAV imagery has become more potential in various applications such as topographic mapping, infrastructure maintenance, construction monitoring, inspection, etc. At the same time, this strongly depends on the photogrammetric process of UAV image block. If the photogrammetric results of an image block, for instance, the estimated image positions and scale information do not meet the required conditions e.g. the geometric accuracy, then these results will be seen as not valid and therefore not recommended to use in farther applications. From this point of view, we are motived to use depth-map data obtained from smartphones/iPad mounted to handheld RTK Rover as additional datasets that should be integrated/implemented into UAV photogrammetric image block.
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Spatial-semantic based mapping for UAV flight planning
In UAV planning, a considerable amount of semantic information is currently not considered; and this may lead to a lack of information that may affect the interaction between UAV and its environment and consequently the flight path may not be generated optimally. From this point of view, we aim in this research area to develop a prototype which enables safe UAV flight planning considering current UAV rules. This prototype should generate a hybrid Dynamic 3D Map (HDM) for safe UAV planning in the form of a link between current geometry of the UAV environment and current contextual and semantic information.
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