• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.132-135
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• 2004

Building information plays a key role in diverse applications such as urban planning, telecommunication and environment monitoring. Automatic building extraction has been a prime interest in the field of GIS and photogrammetry. In this paper, we presented an automatic approach for building extraction from lidar data. The proposed approach is divided into four processes: pre-processing, filtering, segmentation and building extraction. Experimental results showed that the proposed method detected most of buildings with less commission and omission errors.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.99-105
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• 2008

The analysis of LOS(Line-Of-Sight) is defined as an "unobstructed view between two points". The LOS results may be influenced by terrain source, algorithm/interpolation method, etc. In the area denies any access and flight over, LOS results would be doubtful because of low precision of terrain data therefore have limitations of referring to many military applications. Using LIDAR data, LOS Analysis was performed by changing DTED resolution(1$\sim$30m) and LOS distance(50$\sim$2000m). The results of experiment shows that LOS analysis for small area, such as DMZ surveillance, were heavily influenced by DTED resolution.

• ETRI Journal
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• v.28 no.2
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• pp.162-174
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• 2006

Several photogrammetric and geographic information system applications such as surface matching, object recognition, city modeling, environmental monitoring, and change detection deal with multiple versions of the same surface that have been derived from different sources and/or at different times. Surface registration is a necessary procedure prior to the manipulation of these 3D datasets. This need is also applicable in the field of medical imaging, where imaging modalities such as magnetic resonance imaging (MRI) can provide temporal 3D imagery for monitoring disease progression. This paper will present a general automated surface registration procedure that can establish correspondences between conjugate surface elements. Experimental results using light detection and ranging (LIDAR) and MRI data will verify the feasibility, robustness, and accuracy of this approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.44-46
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• 2005

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.513-514
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• 2007

• Korean Journal of Remote Sensing
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• v.32 no.2
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• pp.133-139
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• 2016

Particle depolarization ratios (DPRs) at 440, 675, 870 and 1020 nm are retrieved from AERONET sun/sky radiometer observations at Gosan and Kongju in South Korea. The retrieved results show good agreement with DPRs measured by lidar at 532 nm. High DPRs are found when Asian dust passes through at the upper atmosphere over 2 km above the Earth's surface. In case of lower atmosphere less than 2 km from the ground, DPRs are relatively low due to the small amount of dust particles and mixing of dust with air pollutants.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.77-86
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• 2015

It is difficult to extract small valve automatically in noisy 3D point cloud obtained from LIDAR because small object is affected by noise considerably. In this paper, we assume that the valve is a complex model consisting of torus, cylinder and plane represents handle, rib and center plane to extract a pose of the valve. And to extract the pose, we received additional input: center of the valve. We generated histogram of distance between the center and each points of point cloud, and obtain pose of valve by extracting parameters of handle, rib and center plane. Finally, the valve is reconstructed.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.251-258
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• 2014

We upgraded to utilize a novel method for combining the analog to digital converter and photon-counting measurements for backscatter photon signal of lidar. We have and improve the standard combining method for determination of those conversion factors between analog to digital converter data and photon-counting data measurement which is conducted dead time correction. The combining method and dead time correction method presented here has been successfully applied to experimental data obtained in Gwangju, Korea.