• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1309-1312
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• 2015

본 논문에서는 현대 자율 주행 차량 경진대회에 적용되었던 LIDAR 기반의 다중 물체 추적 알고리즘을 소개한다. 물체 추적은 자율 주행 차량이 외부 환경을 인지하는데 중요한 역할을 한다. 본 논문의 물체 추적 알고리즘은 동시에 여러 개의 물체를 추적할 수 있도록 Multiple Data Association 방식을 사용하였고 순수하게 LIDAR만으로 동작하기 때문에 밤과 낮 모든 경우에 적용 가능하다. 알고리즘은 Clustering, Data Association, State Estimation, Data Arrangement 총 4단계로 이루어져 있으며 본 논문에서는 각 단계별로 알고리즘의 동작 방식을 소개한다. 실제 구현에는 Velodyne사의 HDL-32e이 사용되었고 실제 주행에서 교차로 내의 차량 추적 및 선행 차량의 동향을 추적하는데 적용되었다.

• Atmosphere
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• v.26 no.3
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• pp.461-471
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• 2016

A quality check algorithm for the Weather Information Service Engine pulsed Doppler wind lidar is developed from a view point of spatial and temporal consistencies of observed wind speed. Threshold values for quality check are determined by statistical analysis on the standard deviation of 3-component of wind speed obtained by a wind lidar, and the vertical gradient of horizontal wind speed obtained by a radiosonde system. The algorithm includes carrier-to-noise ratio (CNR) check, data availability check, and vertical gradient of horizontal wind speed check. That is, data sets whose CNR is less than -29 dB, data availability is less than 90%, or vertical gradient of horizontal wind speed is less than $-0.028s^{-1}$ or larger than $0.032s^{-1}$ are classified as 'doubtful', and flagged. The developed quality check algorithm is applied to data obtained at Bucheon station for the period from 1 to 30 September 2015. It is found that the number of 'doubtful' data shows maxima around 2000 m high, but the ratio of 'doubtful' to height-total data increases with increasing height due to atmospheric boundary height, cloud, or rainfall, etc. It is also found that the quality check by data availability is more effective than those by carrier to noise ratio or vertical gradient of horizontal wind speed to remove an erroneous noise data.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.281-286
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• 2005

여러 분야에서 활용성이 증가하고 있는 도시지역에 대한 3차원 모형화 구축은 기존에는 항공사진이나 고해상도 위성영상을 주로 활용하여 왔으나, 최근에는 높은 정밀도를 보장하는 항공LIDAR 측량기법에 대한 연구가 활발히 진행되고 있다. 특히, 다양한 형태, 크기, 종류의 건물들이 존재하는 광범위한 도시지역을 모형화 하기 위하여 정밀도가 높은 LIDAR 자료를 통하여 신속하고 정확하게 현실에 가까운 건물 모형으로 복원하는 기술 개발이 요구되고 있다. 본 연구에서는 LIDAR 관측자료 및 디지털 영상, 수치지도 등의 자료를 활용하여 LIDAR자료의 전처리 과정과 다양한 필터를 적용하여 지면과 비지면 정보를 분류하였으며, LoG 연산자에 의한 건물 경계선 및 특징점 추출기법을 개발하여 도시 지역의 3차원 건물 복원기법을 제안하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.390-395
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• 2009

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.550-555
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• 2003

최근에 Lidar 데이터를 이용한 3차원 위치 정보와 지표면 속성 정보를 취득하는 연구가 많이 진행되고 있다. 높은 위치 정확도, 3차원 데이터 동시 취득, 기존 측정 방식에 비하여 점 데이터 취득의 자동화, 데이터 정확도의 안정성 등으로 인하여 복잡한 지형 및 인공구조물이 존재하는 지역에서 Lidar 데이터의 응용 사례가 많이 나타나고 있으며, 특히 건물 모델링에서 반자동 방식의 디지털 사진측량에 비하여 자동 모델링의 가능성을 보여주고 있다. 일반적으로 Lidar 데이터의 점밀도는 1점/㎡이내이며, 촬영된 스트립을 중복시켜 점밀도를 높이기도 한다. 건물은 크기와 형태가 다양하기 때문에 모델링에 필요한 점밀도를 제시하기는 어렵지만 5점 내외에서 모델링이 가능하다고 알려져 있으며 건물이외에 다른 지형지물에 대한 모델링 연구는 거의 이루어지지 않고 있다. 따라서 본 논문에서는 Lidar 데이터의 점밀도에 따라 지물의 모델링 가능성을 평가하고 효율적인 데이터 취득 방안을 제시하고자 한다.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.25-32
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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) in the first place. 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. 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 modeling 3D building reconstruction.

• Atmosphere
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• v.23 no.4
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• pp.519-526
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• 2013

This study aims to analyze the vertical distribution of Asian dust measured by LIDARs at three weather stations in Baengnyeongdo (BND), Munsan (MS), and Gunsan (GS) during 10~11 May 2010, and thereby investigate their effectiveness. Asian dust passed through from central to south-western part of Korea. Although dust particles were detected over the surface in MS and GS, LIDAR data showed that the Asian dust with non-spherical particles was observed in all of the three regions. It seems that the naked-eye observation could not detect dust over the surface of BND due to the temperature inversion below a height of 0.45 km. During the Asian dust events, the duration time of dust presented 9.5 hr (BND), 19.5 hr (MS), and 24.5 hr (GS), respectively with the longest time in GS, whereas dust altitudes ranged from 0.4 to 1.3 km (BND), 0.1 to 2.8 km and 4.1 to 4.2 km (MS), and 0.2 to 2.0 km (GS), respectively, while showing the highest altitude in MS. Aerosol optical thickness (AOT) retrieved by LIDAR and skyradiometer (SR), located close to the LIDAR sites, was compared. MS (LIDAR) and Seoul (SR) attained the AOT of 0.64 and 0.50, and GS (LIDAR) and Gongju (SR) attained the AOT of 0.38 and 0.54, respectively. As SR-derived angstrom exponents (AE) during the time period determined as Asian dust by LIDAR data were 0.17 in Seoul (near MS) and 0.30 in Gongju (near GS), it can be said that the characteristics of dust particles were appeared. During the study period, depolarization ratio could serve as a useful indicator to determine dust aerosol. But, it still seems essential to conduct further investigation with longer period of data to better describe the discrepancy of AOT between LIDARs and SR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.400-403
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• 2021

Algorithms for predicting the optimal route of vehicles are being actively sudied with the recent development of autonomous driving technology. Companies such as SK, Kakao, and Naver provide services that navigate the optimal route. They predicts the optimal path with information from the users in real time. However, they can predict the optimal route, but not optimal lane route. We proposes a system that navigates the optimal lane path with coordinates data from vehicles using Lidar sensor. The proposed method is a system that guides smooth lanes by acquiring time series coordinate data of a vehicle after performing the Lidar-based object detection method. we demonstrates the performance using actual acquired data from the experimental results.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.103-111
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• 2021

This study addresses a low-cost lidar sensor-based glass feature extraction method for an accurate map representation using statistical moments, i.e. the mean and variance. Since the low-cost lidar sensor produces range-only data without intensity and multi-echo data, there are some difficulties in detecting glass-like objects. In this study, a principle that an incidence angle of a ray emitted from the lidar with respect to a glass surface is close to zero degrees is concerned for glass detection. Besides, all sensor data are preprocessed and clustered, which is represented using statistical moments as glass feature candidates. Glass features are selected among the candidates according to several conditions based on the principle and geometric relation in the global coordinate system. The accumulated glass features are classified according to the distance, which is lastly represented on the map. Several experiments were conducted in glass environments. The results showed that the proposed method accurately extracted and represented glass windows using proper parameters. The parameters were empirically designed and carefully analyzed. In future work, we will implement and perform the conventional SLAM algorithms combined with our glass feature extraction method in glass environments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.143-151
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• 2016

The distribution of seabed rock in the coastal area is relevant to navigation safety and development of ocean resources where it is an essential hydrographic measurement. Currently, the distribution of seabed rock relies on interpretations of water depth data or point based bottom materials survey methods, which have low efficiency. This study uses the airborne bathymetric Lidar data and the hyperspectral image to detect seabed rock in the coastal area of the East Sea. Airborne bathymetric Lidar data detected seabed rocks with texture information that provided 88% accuracy and 24% commission error. Using the airborne hyperspectral image, a classification result of rock and sand gave 79% accuracy, 11% commission error and 7% omission error. The texture data and hyperspectral image were fused to overcome the limitations of individual data. The classification result using fused data showed an improved result with 96% accuracy, 6% commission error and 1% omission error.