• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.841-850
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• 2019

The purpose of this study is to strengthen the capability of rapid mapping for disaster through improving the positioning accuracy of mapping and fusion of multi-sensing point cloud data derived from Unmanned Aerial Vehicles (UAVs) and disaster investigation vehicle. The positioning accuracy was evaluated for two procedures of drone mapping with Agisoft PhotoScan: 1) general geo-referencing by self-calibration, 2) proposed geo-referencing with optimized camera model by using fixed accurate Interior Orientation Parameters (IOPs) derived from indoor camera calibration test and bundle adjustment. The analysis result of positioning accuracy showed that positioning RMS error was improved 2~3 m to 0.11~0.28 m in horizontal and 2.85 m to 0.45 m in vertical accuracy, respectively. In addition, proposed data fusion approach of multi-sensing point cloud with the constraints of the height showed that the point matching error was greatly reduced under about 0.07 m. Accordingly, our proposed data fusion approach will enable us to generate effectively and timelinessly ortho-imagery and high-resolution three dimensional geographic data for national disaster management in the future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.267-277
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• 2019

Photogrammetry and computer vision are identical in determining the three-dimensional coordinates of images taken with a camera, but the two fields are not directly compatible with each other due to differences in camera lens distortion modeling methods and camera coordinate systems. In general, data processing of drone images is performed by bundle block adjustments using computer vision-based software, and then the plotting of the image is performed by photogrammetry-based software for mapping. In this case, we are faced with the problem of converting the model of camera lens distortions into the formula used in photogrammetry. Therefore, this study described the differences between the coordinate systems and lens distortion models used in photogrammetry and computer vision, and proposed a methodology for converting them. In order to verify the conversion formula of the camera lens distortion models, first, lens distortions were added to the virtual coordinates without lens distortions by using the computer vision-based lens distortion models. Then, the distortion coefficients were determined using photogrammetry-based lens distortion models, and the lens distortions were removed from the photo coordinates and compared with the virtual coordinates without the original distortions. The results showed that the root mean square distance was good within 0.5 pixels. In addition, epipolar images were generated to determine the accuracy by applying lens distortion coefficients for photogrammetry. The calculated root mean square error of y-parallax was found to be within 0.3 pixels.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.663-670
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• 2020

In the present study, we assessed the accuracy of aerial triangulation using a UAV (Unmanned Aerial Vehicle) capable of network RTK (Real-Time Kinematic) survey in a disaster situation that may occur in a semi-urban area mixed with buildings. For a reliable survey of check points, they were installed on the roofs of buildings, and static GNSS (Global Navigation Satellite System) survey was conducted for more than four hours. For objective accuracy assessment, coded aerial targets were installed on the check points to be automatically recognized by software. At the instance of image acquisition, the 3D coordinates of the UAV camera were measured using VRS (Virtual Reference Station) method, as a kind of network RTK survey, and the 3-axial angles were achieved using IMU (Inertial Measurement Unit) and gimbal rotation measurement. As a result of estimation and update of the interior and exterior orientation parameters using Agisoft Metashape, the 3D RMSE (Root Mean Square Error) of aerial triangulation ranged from 0.153 m to 0.102 m according to the combination of the image overlap and the angle of the image acquisition. To get higher aerial triangulation accuracy, it was proved to be effective to incorporate oblique images, though it is common to increase the overlap of vertical images. Therefore, to conduct a UAV mapping in an urgent disaster site, it is necessary to acquire oblique images together rather than improving image overlap.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.73-82
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• 2021

According to the government's announcement of the safety management enhancement policy for small and medium-sized private construction sites, the subject of mandatory CCTV installation has been expanded from large construction sites to small and medium-sized construction sites. However, since the existing CCTV at construction sites has been used for simple control for safety management, so research is needed for monitoring of construction sites. Therefore, in this study, three vanishing points were calculated based on a single image taken with a monocular camera, and then a camera matrix containing interior orientation parameters information was determined. And the accuracy was verified by calculating the height of the target object from the height of the reference object. Through height determination experiments using vanishing points based on a monocular camera, it was possible to determine the height of target objects only with a single image without separately surveying of ground control points. As a result of the accuracy evaluation, the root mean square error was ±0.161m. Therefore, it is determined that the progress of construction work at the construction sites can be monitored through the single image taken using the single camera.