|Abstract or Summary
- Small unmanned aircraft systems (UAS) carrying consumer-grade nonmetric cameras are increasingly utilized to generate high-resolution 3D geospatial data. Low cost, ease of operation, widespread availability and low altitude maneuvering capabilities of UAS, as well as the rapid development of technology and methods, make UAS-based photogrammetry applicable to many civil engineering applications such as visualization, 3D mapping, progress monitoring, construction, structural inspection, maintenance, and monitoring. Using computer vision techniques, Structure from Motion (SfM) and Multi-View Stereo (MVS), it is possible to reconstruct 3D scenes from inexpensive, consumer-grade cameras mounted on a UAS. Despite the increasing popularity of UAS, significant research questions remain regarding the accuracy of UAS-based mapping products. In addition, new tools and methods are required to manage and process the vast amount of data generated by UAS. This research explores several novel approaches to address some of these needs in the field of UAS-based photogrammetry.
First, a new approach is developed for supplementing the 3D point clouds derived from UAS imagery with thermal infrared (TIR) imagery. Currently, there are several challenges for reconstructing accurate 3D scenes or point clouds solely from overlapping TIR imagery. For instance, consumer-grade TIR cameras have relatively low resolution and a narrow field view; moreover, such cameras usually generate images with blurred edges and textures. An approach is proposed and evaluated for generating 3D TIR-RGB point clouds utilizing coacquired TIR and RGB images. First, a 3D point cloud is generated using the RGB images; afterward, the TIR data is attributed to the 3D point cloud using a boresight technique. Using the proposed approach, dense point clouds are generated that contain both RGB and TIR information. Such an approach can be beneficial for many thermal mapping and inspection projects, including heat loss inspection, non-destructive testing of structures, and electrical parts inspection for power transmission.
Second, the research examines the feasibility of utilizing UAS-based photogrammetry for detecting the change of above-ground pipelines. Since it is inexpensive to fly, UAS are ideal for inspecting, monitoring, and detecting changes in the sites over time. In the researched approach, repetitive UAS flights were conducted to derive 3D digital terrain models in order to detect and measure the movement of pipelines in the scene. The results are compared with displacement measurements taken from conventional ground surveys using real-time kinematic (RTK) global navigation satellite system (GNSS) receivers and total stations.
Thirdly, this study introduces new dense point cloud quality factors (DPQF) to use as proxy indicators for assessing the accuracy of SfM-MVS dense point clouds. Simulated and empirical experiments are used to assess the accuracy of image-based 3D reconstructed models with respect to different data collection and site condition scenarios. The spatial correlation between the DPQFs and the reconstruction error is investigated and interpreted for multiple experiments. The results of this study show that the DPQF can be a helpful additional field of information for 3D point clouds.
Last, this research introduces a new web GIS tool, named BridgeDex, for management of time-aware series of high-resolution bridge inspection images, such as images collected from handheld digital cameras or cameras on UAS. This tool can be used to manage and query bridge inspection images, bridge reports, and other relevant metadata. This web-based prototype provides the user a simple interface for viewing, panning and zooming in and out of bridge imagery collected over the years as a result of numerous bridge inspections. The tool provides the user an intuitive, organized method for evaluating and managing bridge inspection data.