• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.59-67
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• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.473-478
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• 2004

LIDAH data often include systematic errors, which should be removed by a calibration process. This paper proposes a robust approach to calibrating LIDAR data using natural surfaces as reference data. The uniqueness of this approach is to employ a sophisticated selection scheme so that only a portion of LIDAR points can be used to estimate the bias parameters generating the systematic errors. This approach was applied to calibrating simulated LIDAR data. The results show that the approach can successfully recover the bias parameters and calibrate the data with acceptable RMS errors. Particularly, the parameter recovery model can be easily extended to register image data with LIDAR data.

• ETRI Journal
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• v.33 no.4
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• pp.506-516
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• 2011

In this paper, we develop a registration method to eliminate the geometric inconsistency between the stereo-images and light detection and ranging (LIDAR) data obtained by an airborne multisensor system. This method consists of three steps: registration primitive extraction, correspondence establishment, and exterior orientation parameter (EOP) adjustment. As the primitives, we employ object points and linked edges from the stereo-images and planar patches and intersection edges from the LIDAR data. After extracting these primitives, we establish the correspondence between them, being classified into vertical and horizontal groups. These corresponding pairs are simultaneously incorporated as stochastic constraints into aerial triangulation based on the bundle block adjustment. Finally, the EOPs of the images are adjusted to minimize the inconsistency. The results from the application of our method to real data demonstrate that the inconsistency between both data sets is significantly reduced from the range of 0.5 m to 2 m to less than 0.05 m. Hence, the results show that the proposed method is useful for the data fusion of aerial images and LIDAR data.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.522-525
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• 2005

Light detection and ranging (LIDAR) is one of the effective technologies for monitoring forest inventory, and importance of forestry is increasing because of its function as the sink of green house gases (GHG). This study aims at development of a methodology for better and more accurate estimation of physical parameters of individual trees by removing sudden drops of LIDAR data within a crown. Our study area is located in Aomori prefecture, the northern part of Honshu Island, with the dominant species of Japanese cedar. The results show practicality of our method in the usage of LIDAR data in the field of forest inventory.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.1
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• pp.751-760
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• 2009

This research aims to develop the strategy and method to enhance the reliability of image matching results and improve the efficiency of the matching process by utilizing LIDAR data in the main image matching processes. In this work, we present the methods to utilize LIDAR data in the selection of matching entities, the search for the matched entities and the evaluation of the matching results. The proposed method has been applied to medium-resolution digital aerial images and LIDAR data acquired at the same time. The results have been analyzed in comparison with an existing method using a virtual horizontal surface rather than LIDAR DEM. This analysis indicates that the proposed method can show significantly more improved performance than the existing method. The results of this study can contribute to the improvement of the currently available commercial image matching software and the enhancement of the DEM derived from LIDAR data and matching results.

• 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.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.235-241
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• 2006

The demand of 3D terrain mapping techniques is increasing in many application fields such as CNS(Car Navigation System), web service system, DMB(Digital Multimedia Broadcasting) systems and etc To construct a 3D terrain map, it is a pre-requite step that register data collected from different surveying sources. This Paper Present the methodology to register airborne photo, LIDAR data, and digital map, which are major data sources to create a 3D terrain mao. For this purpose, we developed the generally applicable algorithm that uses linear features to register airborne photos and digital maps to LIDAR data. The algorithm explicitly formulates step-by-step methodologies to establish observation equations for transformation. The results clearly demonstrate the proposed algorithm is appropriate to register these data sources.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.35-42
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• 2005

Precise geometric registration is required in multi-source data fusion process to obtain synergistic results successfully. However, most of the previous studies focus on the assumption of perfect registration or registration in a limited local area with intuitively derived simple geometric model. In this study, therefore, we developed a robust method for geometric registration based on a systematic model that is derived from the geometry associated with the data acquisition processes. The key concept of the proposed approach is to utilize smooth planar patches extracted from LIDAR data as control surfaces to adjust exterior orientation parameters of the aerial images. Registration of the simulated LIDAR data and aerial images was performed. The experimental results show that the RMS value of the geometric discrepancies between two data sets is decreased to less than ${\pm}0.30\;m$ after applying suggested registration method.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.265-268
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• 2008

Lidar(Light Detection and Hanging, Ladar)는 물체에 반사되어 되돌아오는 광학신호를 관측하여 물체와의 거리를 측정하는 센서로 정밀한 3차원 모델 생성 및 도시지역의 변화탐지 등에 효율적으로 적용될 수 있다. 시뮬레이션은 시스템의 동작을 인공적으로 만들어 내고, 이 동작의 실행을 관찰하여 실제 시스템의 특성을 추론하는 일련의 활동으로, 하드웨어의 설계 및 분석, 보완, 성능 평가 등에 효율적으로 이용된다. 본 연구에서는 Lidar 시스템의 원리 및 구조 분석을 통해서 Lidar 데이터를 시뮬레이션하기 위한 소프트웨어를 설계하였다. Lidar의 특성이 시뮬레이션 소프트웨어에 정확하게 구현되도록 하기위해 Lidar 동작과 관련된 내부 및 외부요소를 분석하고 기능에 따라 추상화하여 소프트웨어 모들로 구성하였다. 시스템 내부요소로 송신부 수신부 신호/영상처리부 모델과 외부환경요소로 비행환경 모델, 타겟 모델, 대기 모델을 정의하였다. 또한, Lidar 시스템 실행 중에 발생하는 주요 프로세스를 함수 모듈로 정의함으로서, 모델들 간의 구조적인 관계를 정의하였다. 본 연구의 설계결과는 이후 Lidar시뮬레이션 소프트웨어의 보다 체계적인 구현에 적용될 예정이다.