• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.353-362
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• 2012

3D building modeling from airborne Lidar without model regularization may cause positional errors or topological inconsistency in building models. Regularization of 3D building models, on the other hand, restricts the types of models which can be reconstructed. To resolve these issues, this paper modelled 3D buildings from airborne Lidar by building model regularization which considers more various types of buildings. Building points are first segmented into roof planes by clustering in feature space and segmentation in object space. Then, 3D building models are reconstructed by consecutive adjustment of planes, lines, and points to satisfy parallelism, symmetry, and consistency between model components. The experimental results demonstrated that the method could make more various types of 3d building models with regularity. The effects of regularization on the positional accuracies of models were also analyzed quantitatively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.311-320
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• 2008

A LIDAR can rapidly generate 3D points by densely sampling the surfaces of targets using laser pulses, which has been efficiently utilized to reconstruct 3D models of the targets automatically. Due to this advantage, LIDARs are increasingly applied to the fields of Defense and Security, for examples, being employed to intelligently guided missiles and manned/unmanned reconnaissance planes. For the prior verification of the LIDAR applicability, this study aims at generating simulated LIDAR data. Here, we derived the sensor equation by modelling the geometric relationships between the LIDAR sub-modules, such as GPS, IMU, LS and the systematic errors associated with them. Based on this equation, we developed a program to generate simulated data with the system parameters, the systematic errors, the flight trajectories and attitudes, and the reference terrain model given. This program had been applied to generating simulated LIDAR data for urban areas. By analyzing these simulated data, we verified the accuracy and usefulness of the simulation. The simulator developed in this study will provide economically various test data required for the development of application algorithms and contribute to the optimal establishment of the flight and system parameters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.165-173
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• 2008

This study aims at generating automatically three dimensional geometric models of roads using LIDAR data and a digital map. The main processes in the proposed method are (1) generating a polygon encompassing a road region using a road layer from the digital map, (2) extracting LIDAR points within the road region using the polygon, (3) organizing the points into surface patches and grouping the patches into surface clusters, (4) searching the road surface clusters and generating the surface model from the points linked to the clusters, (5) refining the boundary using a digital map. By applying the proposed method to real data, we successfully generated the linear and surface information of the roads.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.159-166
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• 2017

The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.2
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• pp.33-40
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• 2014

At present, Cadastral map of 2 dimensional is in the stream of changing it to 3 dimensional type supported by GPS and laser techniques. In addition, this steam can be explained at the same time with improvement of equipment of storing much information, support of equipment for imaginative 3D spatial information, and support of equipment of expressing land in 3D Cadastral. This study suggest to apply airborne lidar survey technique on cadastral map to acquire comparably and quickly detailed height of ground. For doing this, this study checked out credibility regarding accuracy of airborne lider survey. After choosing research area, this study has done the airborne lidar survey and acquire the result after surveying Cadastral Comparison Point to check out the accuracy of acquired results. In addition, this study check out the result of Cadastral Comparison Point and airborne lidar survey applied by paired sample t-test based on actual results. The result is that test statistics is 0.322 which is 5 % similar level and null hypothesis cannot be rejected, so this study found out that result of both survey ways are the same. Therefore, the result of airborne lidar survey can be utilized to build up 3D Cadastral information hereafter.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.227-229
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• 2010

This study presents an application of the 3D patch extraction method which is based on gradient-driven properties to obtain 3D planar patches over the terrain and man-made objects from lidar data. The method which was exploited in this study is composed of a sequence of processes: segmentation by slope, initiation of triggering patches by mode selection, and expansion of the triggering patches. Since urban areas contain many planar regions over the terrain surface, application of the method has been experimented to extract 3D planar patches not only from non-terrain objects but also from the terrain. The experimental result shows that the method is efficient to acquire 3D planar patches.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.4
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• pp.28-39
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• 2014

Several studies have been performed globally on the construction of integrated systems that are available for the integrated use of 3D geographic information on terrain and oceans. Research on 3D geographic modeling is also facilitated by the application of Lidar surveying, which enables the highly accurate realization of 3D geographic information for a wide area of land. In addition, a few marine research organizations have been conducting investigations and surveying diverse ocean information for building and applying MGIS(Marine Geographic Information System). However, the construction of integrated geographic information systems for both terrain and oceans has certain limitations resulting from the inconsistency in reference systems and datum levels between two data. Therefore, in this investigation, integrated geospatial information has been realized by using a combined topographical map, after matching the reference systems and datum levels by integration of airborne Lidar data and multi-beam echo sounder data. To verify the accuracy of the integrated geospatial information data, ten randomly selected samples from study areas were selected and analyzed. The results show that the 10 analyzed data samples have an RMSE of 0.46m, which meets the IHO standard(0.5m) for depth accuracy of hydrographic surveys.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.559-570
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• 2014

Backscatter signal observed from the space-borne Light Detection And Ranging (LIDAR) system is providing unique 3-dimensional spatial distribution as well as temporal variations for atmospheric aerosols. In this study, the continuous observations for aerosol profiles were analyzed during a years of 2012 by using a Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP), carried on the Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The statistical analysis on the particulate extinction coefficient and depolarization ratio for each altitude was conducted according to time and space in order to estimate the variation of optical properties of aerosols over Northeast Asia ($E110^{\circ}-140^{\circ}$, $N20^{\circ}$ $-50^{\circ}$). The most frequent altitudes of aerosols are clearly identified and seasonal mean aerosol profiles vary with season. Since relatively high particle depolarization ratios (>0.5) are found during all seasons, it is considered that the non-spherical aerosols mixed with pollution are mainly exists over study area. This study forms initial regional 3-dimensional aerosol information, which will be extended and improved over time for estimation of aerosol climatology and event cases.