• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.79-82
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• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• 한국항행학회논문지
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• 제27권2호
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• pp.190-196
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• 2023

최근 들어, 라이다 기술의 발전에 따라 정확한 거리 측정이 가능해지면서 라이다 기반의 3차원 객체 탐지 네트워크에 대한 관심이 증가하고 있다. 기존의 네트워크는 복셀화 및 다운샘플링 과정에서 공간적인 정보 손실이 발생해 부정확한 위치 추정 결과를 발생시킨다. 본 연구에서는 고수준 특징과 높은 위치 정확도를 동시에 획득하기 위해 어텐션 기반 융합 방식과 카메라-라이다 융합 시스템을 제안한다. 먼저, 그리드 기반의 3차원 객체 탐지 네트워크인 Voxel-RCNN 구조에 어텐션 방식을 도입함으로써, 다중 스케일의 희소 3차원 합성곱 특징을 효과적으로 융합하여 3차원 객체 탐지의 성능을 높인다. 다음으로, 거짓 양성을 제거하기 위해 3차원 객체 탐지 네트워크의 탐지 결과와 이미지상의 2차원 객체 탐지 결과를 결합하는 카메라-라이다 융합 시스템을 제안한다. 제안 알고리즘의 성능평가를 위해 자율주행 분야의 KITTI 데이터 세트를 이용하여 기존 알고리즘과의 비교 실험을 수행한다. 결과적으로, 차량 클래스에 대해 BEV 상의 2차원 객체 탐지와 3차원 객체 탐지 부분에서 성능 향상을 보였으며 특히 Voxel-RCNN보다 차량 Moderate 클래스에 대하여 정확도가 약 0.47% 향상되었다.

• Korean Journal of Geomatics
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• 제4권2호
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• pp.39-45
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• 2004

Building reconstruction attempts to generate geometric and radiometric models of existing buildings usually from sensory data, which have been traditionally aerial or satellite images, more recently airborne LIDAR data, or the combination of these data. Extensive studies on building reconstruction from these data have developed some competitive algorithms with reasonable performance and some degree of automation. Nevertheless, the level of details and completeness of the reconstructed building models often cannot reach the high standards that is now or will be required by various applications in future. Hence, the use of terrestrial sensory data that can provide higher resolution and more complete coverage has been intensively emphasized. We developed a fusion framework for building reconstruction from terrestrial sensory data, that is, points from a laser scanner, images from digital camera, and absolute coordinates from a total station. The proposed approach was then applied to reconstructing a building model from real data sets acquired from a large complex existing building. Based on the experimental results, we assured that the proposed approach cam achieve high resolution and accuracy in building reconstruction. The proposed approach can effectively contribute in developing an operational system producing large urban models for 3D GIS with reasonable resources.

• 인터넷정보학회논문지
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• 제23권6호
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• pp.71-78
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• 2022

본 논문은 3D LiDAR의 포인트 클라우드 데이터를 가공하여 3D 객체탐지를 위한 알고리즘을 제안했다. 기존에 2D LiDAR와 달리 3D LiDAR 기반의 데이터는 너무 방대하며 3차원으로 가공이 힘들었다. 본 논문은 3D LiDAR 기반의 다양한 연구들을 소개하고 3D LiDAR 데이터 처리에 관해 서술하였다. 본 연구에서는 객체탐지를 위해 클러스터링 기법을 활용한 3D LiDAR의 데이터를 가공하는 방법을 제안하며 명확하고 정확한 3D 객체탐지를 위해 카메라와 융합하는 알고리즘 설계하였다. 또한, 3D LiDAR 기반 데이터를 클러스터링하기 위한 모델을 연구하였으며 모델에 따른 하이퍼 파라미터값을 연구하였다. 3D LiDAR 기반 데이터를 클러스터링할 때, DBSCAN 알고리즘이 가장 정확한 결과를 보였으며 DBSCAN의 하이퍼 파라미터값을 비교 분석하였다. 본 연구가 추후 3D LiDAR를 활용한 객체탐지 연구에 도움이 될 것이다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권12호
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• pp.525-534
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• 2023

가스 누출 감지 시스템은 가스의 폭발성과 독성으로 인한 인명 피해를 최소화할 핵심적인 장치이다. 누출 감지 시스템은 대부분 단일 센서를 활용한 방식으로, 가스 센서나 열화상 카메라를 통한 검출 방식으로 진행되고 있다. 이러한 단일 센서 활용의 가스 누출감지 시스템 성능을 고도화하기 위하여, 본 연구에서는 가스 센서와 열화상 이미지 데이터에 멀티모달형 딥러닝을 적용한 연구를 소개한다. 멀티모달 공인 데이터셋인 MultimodalGasData를 통해 기존 논문과의 성능을 비교하였고, 가스 센서와 열화상 카메라의 단일모달 모델을 기반하여 네 가지 멀티모달 모델을 설계 및 학습하였다. 이를 통해 가스 센서와 열화상 카메라는 각각 1D CNN, GasNet 모델이 96.3%와 96.4%의 가장 높은 성능을 보였다. 앞선 두 단일모달 모델을 기반한 Early Fusion 형식의 멀티모달 모델 성능은 99.3%로 가장 높았으며, 또한 기존 논문의 멀티모달 모델 대비 3.3% 높았다. 본 연구의 높은 신뢰성을 갖춘 가스 누출 감지 시스템을 통해 가스 누출로 인한 추가적인 피해가 최소화되길 기대한다.

• 제어로봇시스템학회논문지
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• 제21권7호
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• pp.634-640
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• 2015

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.83-86
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• 2005

This paper presents a method for point-based 3D building reconstruction using LiDAR data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression). If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Based on the roof types identified in automated fashion, the 3D building reconstruction is performed. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LiDAR data and digital map could be a feasible method of modelling 3D building reconstruction.

• Nuclear Engineering and Technology
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• 제50권1호
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• pp.35-42
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• 2018

This article presents clean core criticality calculations and control rod worth calculations for TRIGA (Training, Research, Isotope production-General Atomics) Mark II research reactor benchmark cores using Winfrith Improved Multi-group Scheme-D/4 (WIMS-D/4) and Program for Reactor In-core Analysis using Diffusion Equation (PRIDE) codes. Cores 133 and 134 were analyzed in 2-D (r, ${\theta}$) and 3-D (r, ${\theta}$, z), using WIMS-D/4 and PRIDE codes. Moreover, the influence of cross-section data was also studied using various libraries based on Evaluated Nuclear Data File (ENDF/B-VI.8 and VII.0), Joint Evaluated Fission and Fusion File (JEFF-3.1), Japanese Evaluated Nuclear Data Library (JENDL-3.2), and Joint Evaluated File (JEF-2.2) nuclear data. The simulation results showed that the multiplication factor calculated for all these data libraries is within 1% of the experimental results. The reactivity worth of the control rods of core 134 was also calculated with different homogenization approaches. A comparison was made with experimental and reported Monte Carlo results, and it was found that, using proper homogenization of absorber regions and surrounding fuel regions, the results obtained with PRIDE code are significantly improved.

• 한국분말재료학회지
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• 제29권1호
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• pp.14-21
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• 2022

In the powder bed fusion (PBF) process, a 3D shape is formed by the continuous stacking of very fine powder layers using computer-aided design (CAD) modeling data, following which laser irradiation can be used to fuse the layers forming the desired product. In this method, the main process parameters for manufacturing the desired 3D products are laser power, laser speed, powder form, powder size, laminated thickness, and laser diameter. Stainless steel (STS) 316L exhibits excellent strength at high temperatures, and is also corrosion resistant. Due to this, it is widely used in various additive manufacturing processes, and in the production of corrosion-resistant components with complicated shapes. In this study, rectangular specimens have been manufactured using STS 316L powder via the PBF process. Further, the effect of heat treatment at 800 ℃ on the microstructure and hardness has been investigated.

• International journal of advanced smart convergence
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• 제9권3호
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.