• 한국농공학회논문집
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• 제64권1호
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Computers and Concrete
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• 제33권5호
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• pp.595-603
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• 2024

In the construction industry, there has been a surge in the implementation of high-tech equipment in recent years. Various technologies are being considered as potential solutions for future construction projects. Building information modeling (BIM), which utilizes advanced equipment, is a promising solution among these technologies. The need for safety inspection has also increased with the aging structures. Nevertheless, traditional safety inspection technology falls short of meeting this demand as it heavily relies on the subjective opinions of workers. This inadequacy highlights the need for advancements in existing maintenance technology. Research on building safety inspection using 3D laser scanners has notably increased. Laser scanners that use light detection and ranging (LiDAR) can quickly and accurately acquire producing information, which can be realized through reverse engineering by modeling point cloud data. This study introduces an innovative evaluation system for building safety using a 3D laser scanner. The system was used to assess the safety of an existing three-story building by implementing a reverse engineering technique. The 3D digital data are obtained from the scanner to detect defects and deflections in and outside the building and to create an as-built BIM. Subsequently, the as-built structural model of the building was generated using the reverse engineering approach and used for structural analysis. The acquired information, including deformations and dimensions, is compared with the expected values to evaluate the effectiveness of the proposed technique.

• 로봇학회논문지
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• 제14권2호
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• pp.87-93
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• 2019

This paper presents a 6-DOF relocalization using a 3D laser scanner and a monocular camera. A relocalization problem in robotics is to estimate pose of sensor when a robot revisits the area. A deep convolutional neural network (CNN) is designed to regress 6-DOF sensor pose and trained using both RGB image and 3D point cloud information in end-to-end manner. We generate the new input that consists of RGB and range information. After training step, the relocalization system results in the pose of the sensor corresponding to each input when a new input is received. However, most of cases, mobile robot navigation system has successive sensor measurements. In order to improve the localization performance, the output of CNN is used for measurements of the particle filter that smooth the trajectory. We evaluate our relocalization method on real world datasets using a mobile robot platform.

• 한국측량학회지
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• 제39권2호
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• pp.103-111
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• 2021

MMS (Mobile Mapping System) is being used for HD (High Definition) map construction because it enables fast and accurate data construction, and it is receiving a lot of attention. However, research on the use of MMS in the construction field is insufficient. In this study, road surveying and inspection of construction structures were performed using MMS. Through data acquisition and processing using MMS, point cloud data for the study site was created, and the accuracy was evaluated by comparing with traditional surveying methods. The accuracy analysis results showed a maximum of 0.096m, 0.091m, and 0.093m in the X, Y, and H directions, respectively. Each RMSE was 0.012m, 0.015m, and 0.006m. These result satisfy the accuracy of topographic surveying in the general survey work regulation, indicating that construction surveying using MMS is possible. In addition, a 3D model was created using the design data for the underpass road, and the inspection was performed by comparing it with the MMS data. Through inspection results, deviations in construction can be visually confirmed for the entire underground roadway. The traditional method takes 6 hours for the 4.5km section of the target area, but MMS can significantly shorten the data acquisition time to 0.5 hours. Accurate 3D data is essential data as basic data for future smart construction. With MMS, you can increase the efficiency of construction sites with fast data collection and accuracy.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.246-249
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• 2022

최근 들어 세계적으로 크게 관심을 받는 메타버스 및 몰입형(가상현실, 확장현실, 및 라이트필드) 콘텐츠 서비스의 응용 범위를 확대하기 위해서는 3D 객체의 실시간 전송을 위한 압축 기술이 필요하다. ISO/IEC 23090 MPEG-I Part 5 로 2021 년 표준화 완료된 V-PCC (Video-based Point Cloud Compression)는 이러한 산업계의 관심 및 필요에 의해서 국제 표준화된 동적 3D 포인트 클라우드 객체 부호화 기술이다. V-PCC 기술의 압축 성능은 기존 산업계 기술에 비해 매우 우수하나, 부호화기의 연산 복잡도가 매우 높다는 단점을 가지고 있다. 본 논문에서는 V-PCC 부호화기에서 가장 높은 연산 복잡도를 갖는 법선 추정 알고리즘의 결합 고속화 기법을 제안한다. 법선 추정은 2 개의 알고리즘으로 구성되어 있다. 첫번째는 "방향을 무시하는 법선 추정 알고리즘(normal estimation)"이고, 두번째는 첫번째 알고리즘에서 추정된 법선들을 대상으로 하는 "법선 방향 추정 알고리즘(normal orientation)"이다. 본 논문에서 제안하는 고속화 기법은 2 개 알고리즘을 결합하여 첫번째 법선 추정 알고리즘에서 획득한 부가 정보를 두번째 법선 방향 추정 알고리즘에서 활용함으로써 연산량을 대폭 줄이고, 또한 법선 방향 추정 알고리즘 내의 우선순위 큐 자료구조를 변경하여 추가적인 고속화를 달성한다. 7 개 테스트 영상에 대한 실험 결과, 압축 효율 저하 없이 법선 방향 추정 알고리즘의 속도를 평균 89.2% 향상시킬 수 있다.

• 한국정밀공학회지
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• 제19권6호
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• pp.160-165
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• 2002

A study on digital processes of injection mold in reverse engineering are presented. Reverse engineering is useful fur several cases, where user has no geometry information of object. Laser scanner is used to obtaining 3D coordinates of object. Sequences to process cloud data are described; sampling to reduce number of points, sorting to adjust the point order, and fitting to curve and surface, and so on. Split slide structure of mold is used fur undercut part and high viscosity material. Flow of injection molding are analysed to correct cooling channel and simulate molding conditions. NC tool paths are generated to carve core and cavity. The processes are performed in digital data for reduction of lead time and consecutive geometry data.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2018년도 하계학술대회
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• pp.22-23
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• 2018

본 논문에서는 그래프 간 정합을 이용한 포인트 클라우드 시퀀스 압축 기법을 제안한다. 우선, 그래프를 활용하여 포인트 클라우드 시퀀스의 시변하는 기하학적 구조를 표현하고, 그래프로부터 웨이블릿 변환을 사용하여 추출한 특징 벡터를 매칭하는 방법으로 인접 프레임 간 움직임 예측을 수행한다. 그리고 움직임 예측을 통해 얻은 움직임 벡터 중 정합 점수가 높은 소수의 움직임 벡터를 보간하여 프레임 전체의 움직임 필드를 얻는다. 최종적으로 움직임 정보를 활용하여 얻은 예측 프레임과 타겟 프레임의 차이를 선택적 엔트로피 부호화 방식으로 코딩하여 포인트 클라우드 시퀀스 압축을 수행한다. 실험 결과 제안하는 기법이 3D 포인트 클라우드 시퀀스를 효과적으로 압축함을 확인할 수 있다.

• 한국측량학회지
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• 제39권2호
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• pp.93-101
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• 2021

Recently, with the urban redevelopment and the spread of the planned cities, there is increasing interest in the wind environment, which is related not only to design of buildings and landscaping but also to the comfortability of pedestrians. Numerical analysis for wind environment prediction is underway in many fields, such as dense areas of high-rise building or composition of the apartment complexes, a precisive 3D building model is essentially required in this process. Many studies conducted for wind environment analysis have typically used the method of creating a 3D model by utilizing the building layer included in the GIS (Geographic Information System) data. These data can easily and quickly observe the flow of atmosphere in a wide urban environment, but cannot be suitable for observing precisive flow of atmosphere, and in particular, the effect of a complicated structure of a single building on the flow of atmosphere cannot be calculated. Recently, drone photogrammetry has shown the advantage of being able to automatically perform building modeling based on a large number of images. In this study, we applied photogrammetry technology using a drone to evaluate the flow of atmosphere around two buildings located close to each other. Two 3D models were made into an automatic modeling technique and manual modeling technique. Auto-modeling technique is using an automatically generates a point cloud through photogrammetry and generating models through interpolation, and manual-modeling technique is a manually operated technique that individually generates 3D models based on point clouds. And then the flow of atmosphere for the two models was compared and analyzed. As a result, the wind environment of the two models showed a clear difference, and the model created by auto-modeling showed faster flow of atmosphere than the model created by manual modeling. Also in the case of the 3D mesh generated by auto-modeling showed the limitation of not proceeding an accurate analysis because the precise 3D shape was not reproduced in the closed area such as the porch of the building or the bridge between buildings.

• 지적과 국토정보
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• 제52권1호
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• pp.69-80
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• 2022

드론사진측량은 일반적으로 상공에서 수직 또는 경사로 영상을 획득하므로 3차원 모델링을 위한 목적으로 촬영할 경우 건물의 지면부분에 대한 영상매칭과 점군데이터의 공간정확도가 불량하여 3D메쉬의 완성도가 떨어진다. 따라서 본 연구에서는 이를 극복하기 위해 드론영상과 지상에서 스마트폰 영상을 획득하여 각각의 공간정확도를 분석함은 물론 드론영상에 스마트폰영상을 조합하지 않았을 경우와 조합해석 했을 경우의 정확도 향상과 3D메쉬의 완성도를 평가하였다. 연구결과 드론사진측량의 수평(x,y)정확도는 1/200,000정도로 전통적인 사진측량 정확도와 유사하였다. 또한, 촬영방법에 따른 정확도는 사진맷수의 증가보다 대상물에 대한 촬영각도에 영향을 더 받는 것으로 분석되었다. 스마트폰영상 조합의 경우 정확도에 별다른 영향을 미치지 않았으나 3D메쉬의 완성도는 디지털트윈시티 기준을 만족하는 LoD3급의 3D메쉬를 얻을 수 있었다. 따라서, 드론영상과 지상에서 촬영한 스마트폰 또는 DSLR영상을 조합처리 함으로써 디지털트윈시티를 위한 3D모델 구축에 충분히 활용할 수 있을 것으로 판단된다.

• 한국측량학회지
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• 제36권6호
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• pp.443-449
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• 2018

SLAM (Simultaneous Localization and Mapping) 기반 모바일 매핑 시스템을 활용한 실내 공간의 포인트 클라우드 취득은 건축물의 유지, 관리를 위한 as-built BIM (Building Information Model) 구축의 기초 공정이다. 본 연구에서는 다중 2D 레이저 스캐너로 구성된 모바일 매핑 시스템의 구축을 위한 시뮬레이션 기반 검정(calibration) 표적의 구조 결정 방법을 제안하였다. 2D 레이저 스캐너의 외부 표정요소 검정을 위해 (1) 원형, (2) 사각형, (3) 이중 원형, (4) 이중 사각형 형태의 표적을 구성하였다. 시뮬레이션을 통해 얻어진 각 표적 관측 값을 토대로, 최소제곱법 기반의 외부 표정요소 검정을 수행하였다. 그 결과 사각형 형태의 표적 구조가 주어진 시스템의 검정에 가장 적합한 형태임을 확인하였다. 또한 외부 표정요소 간의 높은 상관성을 확인할 수 있었으며, 표적의 구조에 따른 외부 표정요소의 검정 결과가 상이한 것으로 나타났다.