• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.267-279
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• 2015

In the development of autonomous vehicles, the importance of LIDAR sensors becomes larger. For sensor selection or algorithm development, it is difficult to test expensive LIDAR sensors mounted on a vehicle under various driving environment. In this study, we developed a simulator that is generally applicable for various vehicle LIDAR sensors based on the generalized geometric modeling of the common processes associated with vehicle LIDAR sensors. By configuring this simulator with the specific sensors being widely used, we performed the data simulation and quality analysis. Also, we applied the simulation data to obstacle detection and evaluated the applicability of the selected sensor. The developed simulator enables various experiments and algorithm development in parallel with hardware implementation prior to the deployment and operation of a sensor.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.313-320
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• 2018

The importance of sensors on a self-driving vehicle has rising since it act as eyes for the vehicle. Lidar sensors based on laser technology tend to yield better image quality with more laser channels, thus, it has higher detection accuracy for obstacles, pedistrians, terrain, and other vechicles. However, incorporating more laser channels results higher unit price more than ten times, and this is a major drawback for using high channel lidar sensors on a vehicle for actual consumer market. To come up with this drawback, we propose a method of integrating multiple low channel, low cost lidar sensors acting as one high channel sensor. The result uses four 16 channels lidar sensors with small form factor acting as one bulky 64 channels sensor, which in turn, improves vehicles cosmetic aspects and helps widespread of using the lidar technology for the market.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.199-209
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• 2007

Traditionally, aerial images have been used as main sources for compiling topographic maps. In recent years, lidar data has been exploited as another type of mapping data. Regarding their performances, aerial imagery has the ability to delineate object boundaries but omits much of these boundaries during feature extraction. Lidar provides direct information about heights of object surfaces but have limitations with respect to boundary localization. Considering the characteristics of the sensors, this paper proposes an approach to extracting buildings from lidar and aerial imagery, which is based on the complementary characteristics of optical and range sensors. For detecting building regions, relationships among elevation contours are represented into directional graphs and searched for the contours corresponding to external boundaries of buildings. For generating building models, a wing model is proposed to assemble roof surface patches into a complete building model. Then, building models are projected and checked with features in aerial images. Experimental results show that the proposed approach provides an efficient and accurate way to extract building models.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.199-204
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• 2006

Airborne LIDAR systems have been increasingly used for various applications as an effective surveying mean that can be complementary or alternative to the traditional one based on aerial photos. A LIDAR system is a multi-sensor system consisting of GPS, INS, and a laser scanner and hence the errors associated with the LIDAR data can be significantly affected by not only the errors associated with each individual sensor but also the errors involved in combining these sensors. The analysis about these errors have been performed by some researchers but yet insufficient so that the results can be critically contributed to performing accurate calibration of LIDAR data. In this study, we thus analyze these error sources, derive their mathematical models and perform the sensitivity analysis to assess how significantly each error affects the LIDAR data. The results from this sensitivity analysis in particular can be effectively used to determine the main parameters modelling the systematic errors associated with the LIDAR data for their calibration.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.89-96
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• 2019

360 degree 3-dimensional lidar sensors and vision cameras are commonly used in the development of autonomous driving techniques for automobile, drone, etc. By the way, existing calibration techniques for obtaining th e external transformation of the lidar and the camera sensors have disadvantages in that special calibration objects are used or the object size is too large. In this paper, we introduce a simple calibration method between two sensors using a spherical object. We calculated the sphere center coordinates using four 3-D points selected by RANSAC of the range data of the sphere. The 2-dimensional coordinates of the object center in the camera image are also detected to calibrate the two sensors. Even when the range data is acquired from various angles, the image of the spherical object always maintains a circular shape. The proposed method results in about 2 pixel reprojection error, and the performance of the proposed technique is analyzed by comparing with the existing methods.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.491-500
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• 2004

As the applications of 3D GIS rapidly increase, the need for acquisition and continuos update of urban models is increasingly emphasized. Particularly, building models has been considered as the most crucial component of urban models. Many researchers thus have focused on building extraction from mainly aerial photos or airborne LIDAR data but so far mostly failed to achieve satisfactory results in terms of both completeness and precision because of the intrinsic limitation of the sensory data themselves. Therefore, instead of the airborne sensors, we utilize a terrestrial LIDAR to generate precise and complete building models. This paper presents the overview of the sensors for data acquisition, describes data processing methods for building modelling from the acquired data and summerizes the experimental results.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.4 s.15
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• pp.55-61
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• 2004

Rapid developments in sensor technologies now allow the generation of multi-source topographical data. For many applications, however, the geospatial information provided by individual sensors is not complete, precise, and consistent. To solve these inherent problems, additional diverse sources of complementary data can be used and fused. In this paper, the experiment was done for generation of 3D orthoimage data using LIDAR data and digital camera image. And the results show that 3D orthoimage can be used for the flood monitoring.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.27-33
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• 2017

GPS provides the positioning solution in most areas of the world. However, the position error largely occurs in the urban area due to signal attenuation, signal blockage, and multipath. Although many studies have been carried out to solve this problem, a definite solution has not yet been proposed. Therefore, research is being conducted to solve the vehicle localization problem in the urban environment by converging sensors such as cameras and Light Detection and Ranging (LIDAR). In this paper, the precise vehicle localization using 3D LIDAR (Velodyne HDL-32E) is performed in the urban area. As there are many tall buildings in the urban area and the outer walls of urban buildings consist of planes generally perpendicular to the earth's surface, the outer wall of the building meets at a vertical corner and this vertical corner can be accurately extracted using 3D LIDAR. In this paper, we describe the vertical corner extraction method using 3D LIDAR and perform the precise localization by combining the extracted corner position and GPS/DR information. The driving test was carried out in an about 4.5 km-long section near Teheran-ro, Gangnam. The lateral and longitudinal RMS position errors were 0.146 m and 0.286 m, respectively and showed very accurate localization performance.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.451-459
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• 2011

Fundamentally, there are 5 systems are needed for autonomous navigation of unmanned ground vehicle: Localization, environment perception, path planning, motion planning and vehicle control. Path planning and motion planning are accomplished based on result of the environment perception process. Thus, high reliability of localization and the environment perception will be a criterion that makes a judgment overall autonomous navigation. In this paper, via map matching using vehicle dynamic model and LIDAR sensors, replace high price localization system to new one, and have researched an algorithm that lead to robust autonomous navigation. Finally, all results are verified via actual unmanned ground vehicle tests.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.159-171
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• 2012

Aerosol direct radiative forcing (ADRF) retrieval method was developed by combining data from passive and active satellite sensors. Aerosol optical thickness (AOT) retrieved form the Moderate Resolution Imaging Spectroradiometer (MODIS) as a passive visible sensor and aerosol vertical profile from to the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) as an active laser sensor were investigated an application possibility. Especially, space-born Light Detection and Ranging (Lidar) observation provides a specific knowledge of the optical properties of atmospheric aerosols with spatial, temporal, vertical, and spectral resolutions. On the basis of extensive radiative transfer modeling, it is demonstrated that the use of the aerosol vertical profiles is sensitive to the estimation of ADRF. Throughout the investigation of relationship between aerosol height and ADRF, mean change rates of ADRF per increasing of 1 km aerosol height are smaller at surface than top-of-atmosphere (TOA). As a case study, satellite data for the Asian dust day of March 31, 2007 were used to estimate ADRF. Resulting ADRF values were compared with those retrieved independently from MODIS only data. The absolute difference values are 1.27% at surface level and 4.73% at top of atmosphere (TOA).