• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.55-67
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• 2012

Real-time image georeferencing is required to generate spatial information rapidly from the image sequences acquired by multi-sensor systems. To complement the performance of position/attitude sensors and process in real-time, we should employ on-line aerial triangulation based on a sequential estimation algorithm. In this study, we thus attempt to derive an efficient on-line aerial triangulation algorithm for real-time georeferencing of image sequences. We implemented on-line aerial triangulation using the existing Given transformation update algorithm, and a new inverse normal matrix update algorithm based on observation classification, respectively. To compare the performance of two algorithms in terms of the accuracy and processing time, we applied these algorithms to simulated airborne multi-sensory data. The experimental results indicate that the inverse normal matrix update algorithm shows 40 % higher accuracy in the estimated ground point coordinates and eight times faster processing speed comparing to the Given transformation update algorithm. Therefore, the inverse normal matrix update algorithm is more appropriate for the real-time image georeferencing.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.183-195
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• 2013

Real-time high-precision image georeferencing is important for the realization of image based precise navigation or sophisticated augmented reality. In general, high-precision image georeferencing can be achieved using the conventional simultaneous bundle adjustment algorithm, which can be performed only as post-processing due to its processing time. The recently proposed sequential bundle adjustment algorithm can rapidly produce the results of the similar accuracy and thus opens a possibility of real-time processing. However, since the processing time still increases linearly according to the number of images, if the number of images are too large, its real-time processing is not guaranteed. Based on this algorithm, we propose a modified fast algorithm, the processing time of which is maintained within a limit regardless of the number of images. Since the proposed algorithm considers only the existing images of high correlation with the newly acquired image, it can not only maintain the processing time but also produce accurate results. We applied the proposed algorithm to the images acquired with 1Hz. It is found that the processing time is about 0.02 seconds at the acquisition time of each image in average and the accuracy is about ${\pm}5$ cm on the ground point coordinates in comparison with the results of the conventional simultaneous bundle adjustment algorithm. If this algorithm is converged with a fast image matching algorithm of high reliability, it enables high precision real-time georeferencing of the moving images acquired from a smartphone or UAV by complementing the performance of position and attitude sensors mounted together.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.211-221
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• 2012

To utilize omni-directional images acquired by a rotating line camera for indoor spatial information services, we should register precisely the images with respect to an indoor coordinate system. In this study, we thus develop a georeferencing method to estimate the exterior orientation parameters of an omni-directional image - the position and attitude of the camera at the acquisition time. First, we derive the collinearity equations for the omni-directional image by geometrically modeling the rotating line camera. We then estimate the exterior orientation parameters using the collinearity equations with indoor control points. The experimental results from the application to real data indicate that the exterior orientation parameters is estimated with the precision of 1.4 mm and $0.05^{\circ}$ for the position and attitude, respectively. The residuals are within 3 and 10 pixels in horizontal and vertical directions, respectively. Particularly, the residuals in the vertical direction retain systematic errors mainly due to the lens distortion, which should be eliminated through a camera calibration process. Using omni-directional images georeferenced precisely with the proposed method, we can generate high resolution indoor 3D models and sophisticated augmented reality services based on the models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.507-513
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• 2021

GPR (Ground Penetrating RADAR) is a sensor that inspects the pavement state of roads, sinkholes, and underground pipes. It is widely used in road management. MMS (Mobile Mapping System) creates a detailed and accurate road map of the road surface and its surroundings. If both types of data are built in the same area, it is efficient to construct both ground and underground spatial information at the same time. In addition, since it is possible to grasp the road and important facilities around the road, the location of underground pipelines, etc. without special technology, an intuitive understanding of the site is also possible, which is a useful tool in managing the road or facilities. However, overseas equipment to which this latest technology is applied is expensive and does not fit the domestic situation. LiDAR (Light Detection And Raging) and GNSS/INS (Global Navigation Satellite System / Inertial Navigation System) were synchronized in order to replace overseas developed equipment and to secure original technology to develop domestic equipment in the future, and GPR data was also synchronized to the same GNSS/INS. We developed software that performs georeferencing using the location and attitude information from GNSS/INS at the time of acquiring synchronized GPR data. The experiments were conducted on the road site by dividing the open sky and the non-open sky. The road and surrounding facilities on the ground could be easily checked through the 3D point cloud data acquired through LiDAR. Georeferenced GPR data could also be viewed with a 3D viewer along with point cloud data, and the location of underground facilities could be easily and quickly confirmed through GPR data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.31-38
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• 2012

The geo-referencing accuracy of the images acquired by a UAV based multi-sensor system is affected by the accuracy of the mounting parameters involving the relationship between a camera and a GPS/INS system as well as the performance of a GPS/INS system. Therefore, the estimation of the accurate mounting parameters of a multi-sensor system is important. Currently, we are developing a low altitude multi-sensor system based on a UAV, which can monitor target areas in real time for rapid responses for emergency situations such as natural disasters and accidents. In this study, we suggest a system calibration method for the estimation of the mounting parameters of a multi-sensor system like our system. We also generate simulation data with the sensor specifications of our system, and derive an effective flight configuration and the number of ground control points for accurate and efficient system calibration by applying the proposed method to the simulated data. The experimental results indicate that the proposed method can estimate accurate mounting parameters using over five ground control points and flight configuration composed of six strips. In the near future, we plan to estimate mounting parameters of our system using the proposed method and evaluate the geo-referencing accuracy of the acquired sensory data.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Journal of KIBIM
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• v.13 no.3
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• pp.30-38
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• 2023

Building Information Models(BIM) provides rich geometric and attribute information throughout the entire life cycle of a building and infrastructure object, while Geographic Information System(GIS) enables the detail analysis of urban issues based on the geo-spatial information in support of decision-making. The Integration of BIM and GIS data makes it possible to create a digital twin of the land in order to effectively manage smart cities. In the perspective of integrating BIM data into GIS systems, this study performs literature reviews on georeferencing techniques and identifies limitations in carrying out the georeferencing process using attribute information associated with absolute coordinates probided by Industry Foundation Classes(IFC) as a BIM standard. To address these limitations, an automated georeferencing process is proposed as a pilot study to position a IFC model with the Local Coordinate System(LCS) in GIS environments with the Reference Coordinate System(RCS). An evaluation of the proposed approach over a BIM model demonstrates that the proposed method is expected to be a great help for automatically georeferencing complex BIM models in a GIS environment, and thus provides benefits for efficient and reliable BIM and GIS integration in practice.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.77-80
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• 2010

The demand for human security significantly promotes the development of surveillance applications using a multi-sensor integrated UAV system. For more sophisticated operations, the system should provide a sequence of images rectified in a ground coordinate system in realtime. This rectification requires accurate position and attitude of the camera at the time of exposure of each image, which can be estimated through an Aerial Triangulation process using the GPS/INS data and tie points between adjacent images. In this work, the KLT tracker is utilized to obtain the tie points. To satisfy the realtime requirements, we present an approach to speed up the tracker by supplying the initial guessed positions of tie points based on the exterior orientation. The experimental results show that, when the guessed positions are supplied, the KLT tracker consumed less computational time than the ordinary KLT which is more suitable to be incorporated into the realtime image georeferencing process.

• Spatial Information Research
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• v.23 no.3
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• pp.91-100
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• 2015

We developed a micro UAV based rapid mapping system that provides geospatial information of target areas in a rapid and automatic way. Users can operate the system easily although they are inexperienced in UAV operation and photogrammetric processes. For the aerial data acquisition, we constructed a micro UAV system mounted with a digital camera, a GPS/IMU, and a control board for the sensor integration and synchronization. We also developed a flight planning software and data processing software for the generation of geo-spatial information. The processing software operates automatically with a high speed to perform data quality control, image matching, georeferencing, and orthoimage generation. With the system, we have generated individual ortho-images within 30 minutes from 57 images of 3cm resolution acquired from a target area of $400m{\times}300m$.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.593-603
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• 2010

Most spatial data acquisition systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly reduced by employing an omni-directional camera that is capable of acquiring images in every direction. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by extending the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images. The proposed mathematical model includes three main parts; observation equations based on the collinearity equations newly derived for omni-directional images, stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four different types of mathematical models. With the type where only GCPs are used as the constraints, the proposed BBA can provide the most accurate results, ${\pm}5cm$ of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.