• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2004년도 국내 LBS 기술개발 및 표준화 동향세미나
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• pp.125-131
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• 2004

LiDAR 데이터의 필터링은 원 데이터로부터 건물, 수목 등과 같은 비지면점을 제거하는 과정이며, 이러한 필터링을 통해 DEM을 생성할 수 있다. 대표적인 필터링 방법들로는 분산을 이용한 linear prediction 기법, 주변 점들과의 경사관계를 이용한 slope-based 기법, morphology 필터, local maxima 필터 등이 있으며 이러한 기존의 기법들의 단점을 보완하기 위한 연구가 활발히 진행되고 있다. 대부분의 필터링 기법들은 필터의 크기(윈도우의 크기)와 같은 인자를 대상 지역에 적합하게 사용자가 직접 설정해주어야 한다. 더욱이 복잡한 지형, 지물이 존재하는 지역에 적용하기 위해서는 인자를 변형시켜줘야 하며 특히, 다양한 크기의 건물이 존재하는 지역에 대하여 적용하기 위해서는 가변적인 크기의 필터가 필요하다. 이에 본 논문에서는 다양한 크기의 건물이 존재하는 지역에 대하여 필터의 크기를 변화시키지 않고 필터링을 수행할 수 있는 연산기법을 제안하였다. 본 연구에서는 수목이나 자동차 등과 같은 작은 개체의 제거를 위해 고정된 작은 크기의 윈도우를 가지는 모폴로지 필터를 우선 적용한다. 그 후 건물과 같은 큰 개체의 포인트는 이웃 포인트와의 고도차이를 이용하여 인식하고 이웃에 위치하는 지면 포인트로 대체하며, 갱신된 값이 바로 다음 연산에 반영 되도록 한다. 또한 상, 하, 좌, 우 네 방향에 대하여 라인별로 독립된 연산을 수행한 후에 이들을 비교함으로써 오차를 보정한다.

• 로봇학회논문지
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• 제14권4호
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• Current Optics and Photonics
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• 제3권1호
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• pp.8-15
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• 2019

We describe a common optical system that merges a LADAR system, which generates a point cloud, and a more traditional imaging system operating in the LWIR, which generates image data. The optimum diameter of the entrance pupil was determined by analysis of detection ranges of the LADAR sensor, and the result was applied to design a common optical system using LADAR sensors and LWIR sensors; the performance of these sensors was then evaluated. The minimum detectable signal of the $128{\times}128-pixel$ LADAR detector was calculated as 20.5 nW. The detection range of the LADAR optical system was calculated to be 1,000 m, and according to the results, the optimum diameter of the entrance pupil was determined to be 15.7 cm. The modulation transfer function (MTF) in relation to the diffraction limit of the designed common optical system was analyzed and, according to the results, the MTF of the LADAR optical system was 98.8% at the spatial frequency of 5 cycles per millimeter, while that of the LWIR optical system was 92.4% at the spatial frequency of 29 cycles per millimeter. The detection, recognition, and identification distances of the LWIR optical system were determined to be 5.12, 2.82, and 1.96 km, respectively.

• 자동차안전학회지
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• 제11권2호
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• pp.17-22
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• 2019

This paper describes the tracking algorithm performance evaluation for autonomous vehicle using a safety envelope based path. As the level of autonomous vehicle technologies evolves along with the development of relevant supporting modules including sensors, more advanced methodologies for path generation and tracking are needed. A safety envelope zone, designated as the obstacle free regions between the roadway edges, would be introduced and refined for further application with more detailed specifications. In this paper, the performance of the path tracking algorithm based on the generated path would be evaluated under safety envelop environment. In this process, static obstacle map for safety envelope was created using Lidar based vehicle information such as current vehicle location, speed and yaw rate that were collected under various driving setups at Seoul National University roadways. A level of safety was evaluated through CarSim simulation based on paths generated with two different references: a safety envelope based path and a GPS data based one. A better performance was observed for tracking with the safety envelop based path than that with the GPS based one.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• 한국지리정보학회지
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• 제13권2호
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• pp.119-132
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• 2010

본 연구에서는 울산광역시 태화강 유역에 대하여 건물영향을 고려한 2차원 침수해석을 실시하여 건물영향에 따른 흐름의 양상, 침수심, 침수위 등을 분석하였다. 지형자료는 최근 대도시를 중심으로 구축되고 있는 1m 간격으로 수집된 LiDAR 자료를 바탕으로 10m 간격의 자료를 추출하여 지형자료를 생성하였으며, 수치지형도로부터 추출된 건물자료를 GIS Tool을 활용하여 구축된 지형자료와 합성하여 2차원 침수해석에 적용되는 지형자료를 구성하였다. 파제에 대한 가상의 시나리오를 생성하여 건물영향을 고려한 2차원 침수해석을 실시하였으며, 침수해석 결과에 대한 분석을 통하여 효율적이고 정확한 침수해석 방법을 제안하고자 하였다. 침수면적에 따른 적합도는 건물영향을 고려한 경우와 고려하지 않은 경우를 비교한 결과 90%이하로 떨어지는 것을 확인하였고, 최대 침수심은 건물영향을 고려하지 않은 경우가 건물영향을 고려한 침수해석 결과보다 0.29m 높게 계산되는 것으로 나타났으며, 침수위의 경우 침수심과는 반대로 건물영향을 고려한 경우의 침수해석 결과가 0.49m 높게 나타나는 것으로 분석되었다.

• Korean Journal of Geomatics
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• 제3권1호
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• pp.33-41
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• 2003

Every year, several typhoons hit the Korean peninsula and cause severe damage. For the prevention and accurate estimation of these damages, real time or almost real time flood information is essential. Because of weather conditions, images taken by optic sensors or LIDAR are sometimes not appropriate for an accurate estimation of water areas during typhoon. In this case SAR (Synthetic Aperture Radar) images which are independent of weather condition can be useful for the estimation of flood areas. To get detailed information about floods from satellite imagery, accurate classification of water areas is the most important step. A commonly- and widely-used classification methods is the ML(Maximum Likelihood) method which assumes that the distribution of brightness values of the images follows a Gaussian distribution. The distribution of brightness values of the SAR image, however, usually does not follow a Gaussian distribution. For this reason, in this study the ANN (Artificial Neural Networks) method independent of the statistical characteristics of images is applied to the SAR imagery. RADARS A TSAR images are primarily used for extraction of water areas, and DEM (Digital Elevation Model) is used as supplementary data to evaluate the ground undulation effect. Water areas are also extracted from KOMPSAT image achieved by optic sensors for comparison purpose. Both ANN and ML methods are applied to flat and mountainous areas to extract water areas. The estimated areas from satellite imagery are compared with those of manually extracted results. As a result, the ANN classifier performs better than the ML method when only the SAR image was used as input data, except for mountainous areas. When DEM was used as supplementary data for classification of SAR images, there was a 5.64% accuracy improvement for mountainous area, and a similar result of 0.24% accuracy improvement for flat areas using artificial neural networks.

• 대기
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• 제18권4호
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• pp.267-277
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• 2008

The occurrence and strength of the Asian Dust over the Korea Peninsular have been increased by the expansion of the desert area. For the continuous monitoring of the Asian Dust event, the geostationary satellites provide useful information by detecting the outbreak of the event as well as the long-range transportation of dust. The Infrared Optical Depth Index (IODI) derived from the MTSAT-1R data, indicating a quantitative index of the dust intensity, has been produced in real-time at Korea Meteorological Administration (KMA) since spring of 2007 for the forecast of Asian dust. The data processing algorithm for IODI consists of mainly two steps. The first step is to detect dust area by using brightness temperature difference between two thermal window channels which are influenced with different extinction coefficients by dust. Here we use dynamic threshold values based on the change of surface temperature. In the second step, the IODI is calculated using the ratio between current IR1 brightness temperature and the maximum brightness temperature of the last 10 days which we assume the clear sky. Validation with AOD retrieved from MODIS shows a good agreement over the ocean. Comparison of IODI with the ground based PM10 observation network in Korea shows distinct characteristics depending on the altitude of dust layer estimated from the Lidar data. In the case that the altitude of dust layer is relatively high, the intensity of IODI is larger than that of PM10. On the other hand, when the altitude of dust layer is lower, IODI seems to be relatively small comparing with PM10 measurement.

• 한국측량학회지
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• 제25권6_1호
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• pp.545-555
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• 2007

3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.

• 대한원격탐사학회지
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• 제37권6_2호
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• pp.1793-1801
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• 2021

2015년 1월부터 2020년 6월까지 라이다를 이용하여 측정된 532와 1064 nm의 후방산란계수와 532 nm의 편광소멸도를 이용하여 532 nm의 후방산란계수를 PM₁₀, PM_2.5-10, PM_2.5에 해당하는 세 유형으로 구분하고 지상에서 측정된 질량 농도를 이용하여 각각의 질량소산효율을 산출하였다. 산출된 질량소산효율의 전체 평균값은 PM₁₀, PM_2.5-10, PM_2.5에서 각각 5.1±2.5, 1.7±3.7, 9.3±6.3 m²/g으로 PM_2.5가 가장 높은 값을 보였다. PM₁₀과 PM_2.5의 질량 농도가 낮을 때 평균 이상의 높은 질량소산효율이 산출되었으며 질량 농도가 높아질수록 질량소산효율이 감소되는 경향을 확인하였다. 황사의 혼합 정도에 따른 유형별로 질량소산효율을 산출하였을 때, PM_2.5-10는 황사의 영향으로 오염입자(pollution aerosol, PA)가 2.1±2.8 m²/g으로 오염입자가 주요한 혼합입자(pollution-dominated mixture, PDM), 황사가 주요한 혼합입자 (dust-dominated mixture, DDM), 순수황사 (pure dust, PD)의 1.1±1.8, 1.4±3.3, 1.1±1.5 m²/g보다 두 배 정도 높은 값을 보였다. 하지만, PM_2.5는 9.4±6.5, 9.0±5.8, 10.3±7.5, 9.1±9.0 m²/g으로 유형 구분 없이 비슷한 값을 보였다. PM₁₀의 질량소산효율은 PA, PDM, DDM, PD 에서 각각 5.6±2.9, 4.4±2.0, 3.6±2.9, 2.8±2.4 m²/g으로 황사의 비율이 감소할수록 증가하는 경향을 보였다. 동일한 질량 농도 또는 황사 혼합에 따른 동일한 유형을 보이더라도 PM_2.5/PM₁₀ 값이 낮아질수록 PM_2.5-10의 질량소산효율은 감소하고, PM_2.5의 질량소산효율은 증가하는 경향을 보였다.